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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "b654fa35",
+      "metadata": {},
+      "source": [
+        "# RLSSM Simulator Demo\n",
+        "\n",
+        "This notebook demonstrates the core `ssms.rl` API for simulating reinforcement-learning sequential sampling models (RLSSMs).\n",
+        "\n",
+        "An RLSSM simulation interleaves learning and decision making on every trial:\n",
+        "\n",
+        "1. the learning state, such as Q-values, computes SSM parameters such as drift `v`;\n",
+        "2. an SSM decision process simulates response time and response label;\n",
+        "3. the task environment returns feedback for the chosen action;\n",
+        "4. the learning rule updates before the next trial.\n",
+        "\n",
+        "The examples below show the preset API, explicit component composition, task shorthand configuration, response/action mapping, generated data inspection, plots, and the HSSM config bridge."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "id": "b707135b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "import ssms.rl as rl\n",
+        "\n",
+        "pd.set_option(\"display.max_columns\", 20)\n",
+        "plt.style.use(\"seaborn-v0_8-whitegrid\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "10b5ad21",
+      "metadata": {},
+      "source": [
+        "## 1. Quick Start: Preset Simulation\n",
+        "\n",
+        "The fastest path is to load a named preset, create a simulator, and pass concrete parameter values through `theta`. The built-in `rlssm1` preset combines a Rescorla-Wagner learner, an angle decision process, and a two-arm Bernoulli bandit."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "8a73891a",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Available presets: ['rlssm1']\n",
+            "Model: rlssm1\n",
+            "Decision process: angle\n",
+            "Learning process: RescorlaWagnerDeltaRule\n",
+            "Response labels: (-1, 1)\n",
+            "Response -> action mapping: {-1: 0, 1: 1}\n",
+            "Free parameters: ['rl_alpha', 'scaler', 'a', 'z', 't', 'theta']\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Available presets:\", rl.preset.list())\n",
+        "\n",
+        "bernoulli_config = rl.preset.get(\"rlssm1\")\n",
+        "bernoulli_theta = {\n",
+        "    \"rl_alpha\": 0.1,\n",
+        "    \"scaler\": 2.0,\n",
+        "    \"a\": 1.5,\n",
+        "    \"z\": 0.5,\n",
+        "    \"t\": 0.15,\n",
+        "    \"theta\": 0.1,\n",
+        "}\n",
+        "\n",
+        "print(f\"Model: {bernoulli_config.model_name}\")\n",
+        "print(f\"Decision process: {bernoulli_config.decision_process}\")\n",
+        "print(f\"Learning process: {type(bernoulli_config.learning_process).__name__}\")\n",
+        "print(f\"Response labels: {bernoulli_config.choices}\")\n",
+        "print(f\"Response -> action mapping: {bernoulli_config.response_to_action}\")\n",
+        "print(f\"Free parameters: {bernoulli_config.list_params}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "d9274bcd",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Shape: (4000, 5)\n",
+            "Columns: ['participant_id', 'trial_id', 'rt', 'response', 'feedback']\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>participant_id</th>\n",
+              "      <th>trial_id</th>\n",
+              "      <th>rt</th>\n",
+              "      <th>response</th>\n",
+              "      <th>feedback</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.925990</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3.179487</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.925149</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1.190909</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0.906847</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>3.177333</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.084774</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1.105568</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   participant_id  trial_id        rt  response  feedback\n",
+              "0               0         0  2.925990         1       0.0\n",
+              "1               0         1  3.179487         1       0.0\n",
+              "2               0         2  0.925149        -1       0.0\n",
+              "3               0         3  1.190909        -1       1.0\n",
+              "4               0         4  0.906847        -1       0.0\n",
+              "5               0         5  3.177333        -1       1.0\n",
+              "6               0         6  3.084774         1       0.0\n",
+              "7               0         7  1.105568        -1       1.0"
+            ]
+          },
+          "execution_count": 27,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "bernoulli_sim = rl.Simulator(bernoulli_config)\n",
+        "bernoulli_data = bernoulli_sim.simulate(\n",
+        "    theta=bernoulli_theta,\n",
+        "    n_trials=200,\n",
+        "    n_participants=20,\n",
+        "    random_state=42,\n",
+        ")\n",
+        "\n",
+        "print(f\"Shape: {bernoulli_data.shape}\")\n",
+        "print(f\"Columns: {list(bernoulli_data.columns)}\")\n",
+        "bernoulli_data.head(8)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "42e52b3d",
+      "metadata": {},
+      "source": [
+        "## 2. Configuring Models\n",
+        "\n",
+        "The simulator API is compositional: a `ModelConfig` combines one decision process, one learning process, and one task environment. The config describes model structure; concrete parameter values stay in `theta`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "id": "7a1aa457",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Model: rlssm_angle_dual_alpha_gaussian\n",
+            "Learning process: RescorlaWagnerDualAlphaRule\n",
+            "Free parameters: ['rl_alpha', 'rl_alpha_neg', 'scaler', 'a', 'z', 't', 'theta']\n",
+            "Computed SSM parameters: ['v']\n"
+          ]
+        }
+      ],
+      "source": [
+        "gaussian_config = rl.ModelConfig(\n",
+        "    model_name=\"rlssm_angle_dual_alpha_gaussian\",\n",
+        "    description=\"Dual-alpha Rescorla-Wagner + angle + Gaussian bandit\",\n",
+        "    decision_process=\"angle\",\n",
+        "    learning_process=rl.learning.RescorlaWagnerDualAlphaRule(),\n",
+        "    task_environment=rl.env.Bandit.gaussian(\n",
+        "        means=[0.8, 0.2],\n",
+        "        sds=[0.25, 0.25],\n",
+        "        response_labels=[-1, 1],\n",
+        "    ),\n",
+        ")\n",
+        "\n",
+        "gaussian_theta = {\n",
+        "    \"rl_alpha\": 0.3,\n",
+        "    \"rl_alpha_neg\": 0.1,\n",
+        "    \"scaler\": 2.0,\n",
+        "    \"a\": 1.5,\n",
+        "    \"z\": 0.5,\n",
+        "    \"t\": 0.3,\n",
+        "    \"theta\": 0.2,\n",
+        "}\n",
+        "\n",
+        "print(f\"Model: {gaussian_config.model_name}\")\n",
+        "print(f\"Learning process: {type(gaussian_config.learning_process).__name__}\")\n",
+        "print(f\"Free parameters: {gaussian_config.list_params}\")\n",
+        "print(f\"Computed SSM parameters: {gaussian_config.learning_process.computed_params}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "id": "ec6a47ef",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TaskConfig built: Bandit\n",
+            "Registered task shorthands: ['bandit']\n"
+          ]
+        }
+      ],
+      "source": [
+        "taskconfig_config = rl.ModelConfig(\n",
+        "    model_name=\"taskconfig_bernoulli_rlssm\",\n",
+        "    description=\"TaskConfig shorthand for a Bernoulli bandit\",\n",
+        "    decision_process=\"angle\",\n",
+        "    learning_process=rl.learning.RescorlaWagnerDeltaRule(),\n",
+        "    task_environment=rl.env.TaskConfig(\n",
+        "        task=\"bandit\",\n",
+        "        reward=\"bernoulli\",\n",
+        "        probabilities=[0.65, 0.35],\n",
+        "        response_labels=[-1, 1],\n",
+        "    ),\n",
+        ")\n",
+        "\n",
+        "print(f\"TaskConfig built: {type(taskconfig_config.task_environment).__name__}\")\n",
+        "print(f\"Registered task shorthands: {rl.env.registered_tasks()}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "id": "6aff8252",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>example</th>\n",
+              "      <th>model</th>\n",
+              "      <th>decision</th>\n",
+              "      <th>learning</th>\n",
+              "      <th>reward</th>\n",
+              "      <th>response_labels</th>\n",
+              "      <th>computed</th>\n",
+              "      <th>free_params</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>preset</td>\n",
+              "      <td>rlssm1</td>\n",
+              "      <td>angle</td>\n",
+              "      <td>RescorlaWagnerDeltaRule</td>\n",
+              "      <td>Bernoulli</td>\n",
+              "      <td>(-1, 1)</td>\n",
+              "      <td>(v,)</td>\n",
+              "      <td>(rl_alpha, scaler, a, z, t, theta)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>components</td>\n",
+              "      <td>rlssm_angle_dual_alpha_gaussian</td>\n",
+              "      <td>angle</td>\n",
+              "      <td>RescorlaWagnerDualAlphaRule</td>\n",
+              "      <td>Gaussian</td>\n",
+              "      <td>(-1, 1)</td>\n",
+              "      <td>(v,)</td>\n",
+              "      <td>(rl_alpha, rl_alpha_neg, scaler, a, z, t, theta)</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>TaskConfig</td>\n",
+              "      <td>taskconfig_bernoulli_rlssm</td>\n",
+              "      <td>angle</td>\n",
+              "      <td>RescorlaWagnerDeltaRule</td>\n",
+              "      <td>Bernoulli</td>\n",
+              "      <td>(-1, 1)</td>\n",
+              "      <td>(v,)</td>\n",
+              "      <td>(rl_alpha, scaler, a, z, t, theta)</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      example                            model decision  \\\n",
+              "0      preset                           rlssm1    angle   \n",
+              "1  components  rlssm_angle_dual_alpha_gaussian    angle   \n",
+              "2  TaskConfig       taskconfig_bernoulli_rlssm    angle   \n",
+              "\n",
+              "                      learning     reward response_labels computed  \\\n",
+              "0      RescorlaWagnerDeltaRule  Bernoulli         (-1, 1)     (v,)   \n",
+              "1  RescorlaWagnerDualAlphaRule   Gaussian         (-1, 1)     (v,)   \n",
+              "2      RescorlaWagnerDeltaRule  Bernoulli         (-1, 1)     (v,)   \n",
+              "\n",
+              "                                        free_params  \n",
+              "0                (rl_alpha, scaler, a, z, t, theta)  \n",
+              "1  (rl_alpha, rl_alpha_neg, scaler, a, z, t, theta)  \n",
+              "2                (rl_alpha, scaler, a, z, t, theta)  "
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "def summarize_config(label, config, reward_kind):\n",
+        "    return {\n",
+        "        \"example\": label,\n",
+        "        \"model\": config.model_name,\n",
+        "        \"decision\": config.decision_process,\n",
+        "        \"learning\": type(config.learning_process).__name__,\n",
+        "        \"reward\": reward_kind,\n",
+        "        \"response_labels\": tuple(config.choices),\n",
+        "        \"computed\": tuple(config.learning_process.computed_params),\n",
+        "        \"free_params\": tuple(config.list_params),\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "config_summary = pd.DataFrame(\n",
+        "    [\n",
+        "        summarize_config(\"preset\", bernoulli_config, \"Bernoulli\"),\n",
+        "        summarize_config(\"components\", gaussian_config, \"Gaussian\"),\n",
+        "        summarize_config(\"TaskConfig\", taskconfig_config, \"Bernoulli\"),\n",
+        "    ]\n",
+        ")\n",
+        "config_summary"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d665c781",
+      "metadata": {},
+      "source": [
+        "## 3. Response Labels vs Learning Actions\n",
+        "\n",
+        "SSM decision processes can emit response labels such as `-1` and `1`. Learning rules and task environments use zero-based action indices. `response_mapping=\"auto\"` derives the mapping from the environment's response-label order, and `include_action=True` can add the derived action to the simulated data for inspection."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "id": "9ee7834f",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Response -> action mapping: {-1: 0, 1: 1}\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>participant_id</th>\n",
+              "      <th>trial_id</th>\n",
+              "      <th>rt</th>\n",
+              "      <th>response</th>\n",
+              "      <th>action</th>\n",
+              "      <th>feedback</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.256809</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.437518</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2.189805</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0.987093</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1.034955</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1.244467</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>2.295517</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>3.380327</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>3.858962</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>2.201911</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>0.482978</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>0</td>\n",
+              "      <td>11</td>\n",
+              "      <td>2.497566</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    participant_id  trial_id        rt  response  action  feedback\n",
+              "0                0         0  1.256809         1       1       1.0\n",
+              "1                0         1  1.437518         1       1       0.0\n",
+              "2                0         2  2.189805        -1       0       0.0\n",
+              "3                0         3  0.987093         1       1       1.0\n",
+              "4                0         4  1.034955         1       1       0.0\n",
+              "5                0         5  1.244467         1       1       1.0\n",
+              "6                0         6  2.295517        -1       0       1.0\n",
+              "7                0         7  3.380327         1       1       0.0\n",
+              "8                0         8  3.858962         1       1       0.0\n",
+              "9                0         9  2.201911        -1       0       1.0\n",
+              "10               0        10  0.482978        -1       0       1.0\n",
+              "11               0        11  2.497566        -1       0       1.0"
+            ]
+          },
+          "execution_count": 31,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "mapping_config = rl.ModelConfig(\n",
+        "    model_name=\"response_action_demo\",\n",
+        "    description=\"Expose derived actions for response-label inspection\",\n",
+        "    decision_process=\"angle\",\n",
+        "    learning_process=rl.learning.RescorlaWagnerDeltaRule(),\n",
+        "    task_environment=rl.env.Bandit.bernoulli(\n",
+        "        probabilities=[0.7, 0.3],\n",
+        "        response_labels=[-1, 1],\n",
+        "    ),\n",
+        "    include_action=True,\n",
+        ")\n",
+        "\n",
+        "mapping_data = rl.Simulator(mapping_config).simulate(\n",
+        "    theta=bernoulli_theta,\n",
+        "    n_trials=12,\n",
+        "    n_participants=1,\n",
+        "    random_state=7,\n",
+        ")\n",
+        "\n",
+        "print(f\"Response -> action mapping: {mapping_config.response_to_action}\")\n",
+        "mapping_data[[\"participant_id\", \"trial_id\", \"rt\", \"response\", \"action\", \"feedback\"]]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7b3927d5",
+      "metadata": {},
+      "source": [
+        "## 4. Worked Simulation Variants\n",
+        "\n",
+        "The remaining sections compare two simulated datasets: the Bernoulli preset and an explicit Gaussian/dual-alpha model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "id": "86425400",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gaussian data shape: (4000, 5)\n",
+            "Gaussian feedback range: -0.309 to 1.947\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>participant_id</th>\n",
+              "      <th>trial_id</th>\n",
+              "      <th>rt</th>\n",
+              "      <th>response</th>\n",
+              "      <th>feedback</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.533501</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0.788125</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1.006936</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0.772137</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2.617165</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>1.028608</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1.383640</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0.907379</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1.128517</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0.937621</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2.942698</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0.381145</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0.865283</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0.915096</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1.623786</td>\n",
+              "      <td>-1</td>\n",
+              "      <td>0.817361</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   participant_id  trial_id        rt  response  feedback\n",
+              "0               0         0  1.533501        -1  0.788125\n",
+              "1               0         1  1.006936        -1  0.772137\n",
+              "2               0         2  2.617165        -1  1.028608\n",
+              "3               0         3  1.383640        -1  0.907379\n",
+              "4               0         4  1.128517        -1  0.937621\n",
+              "5               0         5  2.942698        -1  0.381145\n",
+              "6               0         6  0.865283        -1  0.915096\n",
+              "7               0         7  1.623786        -1  0.817361"
+            ]
+          },
+          "execution_count": 32,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gaussian_sim = rl.Simulator(gaussian_config)\n",
+        "gaussian_data = gaussian_sim.simulate(\n",
+        "    theta=gaussian_theta,\n",
+        "    n_trials=200,\n",
+        "    n_participants=20,\n",
+        "    random_state=123,\n",
+        ")\n",
+        "\n",
+        "print(f\"Gaussian data shape: {gaussian_data.shape}\")\n",
+        "print(\n",
+        "    \"Gaussian feedback range: \"\n",
+        "    f\"{gaussian_data['feedback'].min():.3f} to {gaussian_data['feedback'].max():.3f}\"\n",
+        ")\n",
+        "gaussian_data.head(8)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "id": "0b0ac7ed",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "rlssm_datasets = {\n",
+        "    \"bernoulli\": {\n",
+        "        \"label\": \"Bernoulli bandit + single alpha\",\n",
+        "        \"data\": bernoulli_data,\n",
+        "        \"config\": bernoulli_config,\n",
+        "        \"theta\": bernoulli_theta,\n",
+        "        \"reward_values\": [0.7, 0.3],\n",
+        "        \"target_label\": \"high reward-probability arm\",\n",
+        "    },\n",
+        "    \"gaussian\": {\n",
+        "        \"label\": \"Gaussian bandit + dual alpha\",\n",
+        "        \"data\": gaussian_data,\n",
+        "        \"config\": gaussian_config,\n",
+        "        \"theta\": gaussian_theta,\n",
+        "        \"reward_values\": [0.8, 0.2],\n",
+        "        \"target_label\": \"high mean-reward arm\",\n",
+        "    },\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "237cecda",
+      "metadata": {},
+      "source": [
+        "## 5. Inspecting Generated Data\n",
+        "\n",
+        "Simulator output is a participant-by-trial panel with response times, SSM response labels, feedback, and optional task fields. These checks focus on the shape HSSM expects: participant-contiguous rows, balanced trial counts, and required columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "id": "9c26eff7",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>dataset</th>\n",
+              "      <th>rows</th>\n",
+              "      <th>participants</th>\n",
+              "      <th>trials_per_participant</th>\n",
+              "      <th>balanced</th>\n",
+              "      <th>sorted</th>\n",
+              "      <th>missing_required_columns</th>\n",
+              "      <th>response_labels</th>\n",
+              "      <th>feedback_min</th>\n",
+              "      <th>feedback_max</th>\n",
+              "      <th>omissions</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Bernoulli bandit + single alpha</td>\n",
+              "      <td>4000</td>\n",
+              "      <td>20</td>\n",
+              "      <td>(200,)</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>(-1, 1)</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Gaussian bandit + dual alpha</td>\n",
+              "      <td>4000</td>\n",
+              "      <td>20</td>\n",
+              "      <td>(200,)</td>\n",
+              "      <td>True</td>\n",
+              "      <td>True</td>\n",
+              "      <td>[]</td>\n",
+              "      <td>(-1, 1)</td>\n",
+              "      <td>-0.309363</td>\n",
+              "      <td>1.946633</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                           dataset  rows  participants trials_per_participant  \\\n",
+              "0  Bernoulli bandit + single alpha  4000            20                 (200,)   \n",
+              "1     Gaussian bandit + dual alpha  4000            20                 (200,)   \n",
+              "\n",
+              "   balanced  sorted missing_required_columns response_labels  feedback_min  \\\n",
+              "0      True    True                       []         (-1, 1)      0.000000   \n",
+              "1      True    True                       []         (-1, 1)     -0.309363   \n",
+              "\n",
+              "   feedback_max  omissions  \n",
+              "0      1.000000          0  \n",
+              "1      1.946633          0  "
+            ]
+          },
+          "execution_count": 34,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "REQUIRED_COLUMNS = {\"participant_id\", \"trial_id\", \"rt\", \"response\", \"feedback\"}\n",
+        "\n",
+        "\n",
+        "def inspect_dataset(dataset_key):\n",
+        "    dataset = rlssm_datasets[dataset_key]\n",
+        "    df = dataset[\"data\"]\n",
+        "    sorted_panel = df.equals(df.sort_values([\"participant_id\", \"trial_id\"]).reset_index(drop=True))\n",
+        "    trials_per_participant = df.groupby(\"participant_id\").size()\n",
+        "    missing_required = sorted(REQUIRED_COLUMNS - set(df.columns))\n",
+        "    return {\n",
+        "        \"dataset\": dataset[\"label\"],\n",
+        "        \"rows\": len(df),\n",
+        "        \"participants\": df[\"participant_id\"].nunique(),\n",
+        "        \"trials_per_participant\": tuple(sorted(trials_per_participant.unique())),\n",
+        "        \"balanced\": trials_per_participant.nunique() == 1,\n",
+        "        \"sorted\": sorted_panel,\n",
+        "        \"missing_required_columns\": missing_required,\n",
+        "        \"response_labels\": tuple(sorted(df[\"response\"].unique())),\n",
+        "        \"feedback_min\": df[\"feedback\"].min(),\n",
+        "        \"feedback_max\": df[\"feedback\"].max(),\n",
+        "        \"omissions\": int((df[\"response\"] == -999).sum()),\n",
+        "    }\n",
+        "\n",
+        "\n",
+        "inspection = pd.DataFrame(\n",
+        "    [inspect_dataset(\"bernoulli\"), inspect_dataset(\"gaussian\")]\n",
+        ")\n",
+        "inspection"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "id": "bb7f24da",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>dataset</th>\n",
+              "      <th>mean_rt</th>\n",
+              "      <th>median_rt</th>\n",
+              "      <th>mean_feedback</th>\n",
+              "      <th>p_response_-1</th>\n",
+              "      <th>p_response_1</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Bernoulli bandit + single alpha</td>\n",
+              "      <td>1.583278</td>\n",
+              "      <td>1.316670</td>\n",
+              "      <td>0.63850</td>\n",
+              "      <td>0.85225</td>\n",
+              "      <td>0.14775</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Gaussian bandit + dual alpha</td>\n",
+              "      <td>1.381850</td>\n",
+              "      <td>1.205391</td>\n",
+              "      <td>0.76183</td>\n",
+              "      <td>0.93100</td>\n",
+              "      <td>0.06900</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                           dataset   mean_rt  median_rt  mean_feedback  \\\n",
+              "0  Bernoulli bandit + single alpha  1.583278   1.316670        0.63850   \n",
+              "1     Gaussian bandit + dual alpha  1.381850   1.205391        0.76183   \n",
+              "\n",
+              "   p_response_-1  p_response_1  \n",
+              "0        0.85225       0.14775  \n",
+              "1        0.93100       0.06900  "
+            ]
+          },
+          "execution_count": 35,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "summary_rows = []\n",
+        "for dataset_key, dataset in rlssm_datasets.items():\n",
+        "    df = dataset[\"data\"]\n",
+        "    summary_rows.append(\n",
+        "        {\n",
+        "            \"dataset\": dataset[\"label\"],\n",
+        "            \"mean_rt\": df[\"rt\"].mean(),\n",
+        "            \"median_rt\": df[\"rt\"].median(),\n",
+        "            \"mean_feedback\": df[\"feedback\"].mean(),\n",
+        "            \"p_response_-1\": (df[\"response\"] == -1).mean(),\n",
+        "            \"p_response_1\": (df[\"response\"] == 1).mean(),\n",
+        "        }\n",
+        "    )\n",
+        "\n",
+        "pd.DataFrame(summary_rows)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c53f2b57",
+      "metadata": {},
+      "source": [
+        "## 6. Visualizing Learning and Decisions\n",
+        "\n",
+        "The plots below summarize group-level learning, participant-level latent dynamics reconstructed from responses and feedback, and response-time distributions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "id": "4b281020",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1400x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "def target_response(dataset):\n",
+        "    reward_values = dataset[\"reward_values\"]\n",
+        "    target_action = int(np.argmax(reward_values))\n",
+        "    return dataset[\"config\"].task_environment.response_labels[target_action]\n",
+        "\n",
+        "\n",
+        "def plot_learning_curve(dataset_key, window=10, ax=None):\n",
+        "    dataset = rlssm_datasets[dataset_key]\n",
+        "    df = dataset[\"data\"]\n",
+        "    target = target_response(dataset)\n",
+        "\n",
+        "    learning_curve = (\n",
+        "        df.assign(chose_target=(df[\"response\"] == target).astype(float))\n",
+        "        .groupby(\"trial_id\", as_index=False)[\"chose_target\"]\n",
+        "        .mean()\n",
+        "        .rename(columns={\"chose_target\": \"p_target\"})\n",
+        "    )\n",
+        "\n",
+        "    if ax is None:\n",
+        "        _, ax = plt.subplots(figsize=(8, 4))\n",
+        "\n",
+        "    ax.plot(\n",
+        "        learning_curve[\"trial_id\"],\n",
+        "        learning_curve[\"p_target\"],\n",
+        "        alpha=0.35,\n",
+        "        linewidth=1.2,\n",
+        "        label=\"trial-wise mean\",\n",
+        "    )\n",
+        "    ax.plot(\n",
+        "        learning_curve[\"trial_id\"],\n",
+        "        learning_curve[\"p_target\"].rolling(window=window, min_periods=1).mean(),\n",
+        "        linewidth=2.2,\n",
+        "        label=f\"rolling mean (window={window})\",\n",
+        "    )\n",
+        "    ax.axhline(0.5, color=\"gray\", linestyle=\"--\", alpha=0.6, label=\"chance\")\n",
+        "    ax.set_xlabel(\"Trial\")\n",
+        "    ax.set_ylabel(f\"P(choose {dataset['target_label']})\")\n",
+        "    ax.set_title(dataset[\"label\"])\n",
+        "    ax.set_ylim(0, 1)\n",
+        "    ax.legend()\n",
+        "    return ax\n",
+        "\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(14, 4), sharey=True)\n",
+        "plot_learning_curve(\"bernoulli\", ax=axes[0])\n",
+        "plot_learning_curve(\"gaussian\", ax=axes[1])\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "id": "ce5f9e41",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x800 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x800 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "def replay_learning_dynamics(dataset_key, participant_id=0):\n",
+        "    dataset = rlssm_datasets[dataset_key]\n",
+        "    df = dataset[\"data\"]\n",
+        "    config = dataset[\"config\"]\n",
+        "    theta = dataset[\"theta\"]\n",
+        "    subj_data = df[df[\"participant_id\"] == participant_id].copy().reset_index(drop=True)\n",
+        "\n",
+        "    q_vals = np.full(config.task_environment.n_arms, 0.5, dtype=float)\n",
+        "    q_history = [q_vals.copy()]\n",
+        "    drift_history = []\n",
+        "\n",
+        "    for _, row in subj_data.iterrows():\n",
+        "        drift_history.append((q_vals[1] - q_vals[0]) * theta[\"scaler\"])\n",
+        "        if int(row[\"response\"]) == -999:\n",
+        "            q_history.append(q_vals.copy())\n",
+        "            continue\n",
+        "\n",
+        "        action = config.response_to_action[int(row[\"response\"])]\n",
+        "        reward = float(row[\"feedback\"])\n",
+        "        delta = reward - q_vals[action]\n",
+        "        alpha = theta[\"rl_alpha_neg\"] if delta < 0 and \"rl_alpha_neg\" in theta else theta[\"rl_alpha\"]\n",
+        "        q_vals[action] += alpha * delta\n",
+        "        q_history.append(q_vals.copy())\n",
+        "\n",
+        "    return subj_data, np.array(q_history), np.array(drift_history)\n",
+        "\n",
+        "\n",
+        "def plot_learning_dynamics(dataset_key, participant_id=0):\n",
+        "    dataset = rlssm_datasets[dataset_key]\n",
+        "    config = dataset[\"config\"]\n",
+        "    labels = list(config.task_environment.response_labels)\n",
+        "    target = target_response(dataset)\n",
+        "    other = [label for label in labels if label != target][0]\n",
+        "    subj_data, q_history, drift_history = replay_learning_dynamics(\n",
+        "        dataset_key, participant_id=participant_id\n",
+        "    )\n",
+        "\n",
+        "    fig, axes = plt.subplots(3, 1, figsize=(10, 8), sharex=True)\n",
+        "\n",
+        "    for action, label in enumerate(labels):\n",
+        "        axes[0].plot(q_history[:, action], label=f\"Q[action {action}, response {label}]\", alpha=0.85)\n",
+        "    axes[0].set_ylabel(\"Q-value\")\n",
+        "    axes[0].set_title(f\"{dataset['label']}: participant {participant_id}\")\n",
+        "    axes[0].legend()\n",
+        "    axes[0].axhline(0.5, color=\"gray\", linestyle=\"--\", alpha=0.3)\n",
+        "\n",
+        "    axes[1].plot(drift_history, color=\"C2\", alpha=0.8)\n",
+        "    axes[1].set_ylabel(\"Drift v\")\n",
+        "    axes[1].set_title(\"Drift computed before each trial update\")\n",
+        "    axes[1].axhline(0, color=\"gray\", linestyle=\"--\", alpha=0.3)\n",
+        "\n",
+        "    responses = subj_data[\"response\"].to_numpy()\n",
+        "    rewards = subj_data[\"feedback\"].to_numpy()\n",
+        "    trials = np.arange(len(responses))\n",
+        "    axes[2].scatter(\n",
+        "        trials[responses == target],\n",
+        "        rewards[responses == target],\n",
+        "        c=\"C0\",\n",
+        "        s=12,\n",
+        "        alpha=0.55,\n",
+        "        label=f\"response {target} ({dataset['target_label']})\",\n",
+        "    )\n",
+        "    axes[2].scatter(\n",
+        "        trials[responses == other],\n",
+        "        rewards[responses == other],\n",
+        "        c=\"C1\",\n",
+        "        s=12,\n",
+        "        alpha=0.55,\n",
+        "        label=f\"response {other}\",\n",
+        "    )\n",
+        "    axes[2].set_ylabel(\"Feedback\")\n",
+        "    axes[2].set_xlabel(\"Trial\")\n",
+        "    axes[2].set_title(\"Observed choices and feedback\")\n",
+        "    axes[2].legend(markerscale=2.5)\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "for dataset_key in [\"bernoulli\", \"gaussian\"]:\n",
+        "    plot_learning_dynamics(dataset_key, participant_id=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "id": "cdff9fed",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1400x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "def plot_rt_distributions(dataset_key, ax=None):\n",
+        "    dataset = rlssm_datasets[dataset_key]\n",
+        "    df = dataset[\"data\"]\n",
+        "    labels = list(dataset[\"config\"].task_environment.response_labels)\n",
+        "    target = target_response(dataset)\n",
+        "\n",
+        "    if ax is None:\n",
+        "        _, ax = plt.subplots(figsize=(8, 4))\n",
+        "\n",
+        "    for label, color in zip(labels, [\"C0\", \"C1\", \"C2\", \"C3\"]):\n",
+        "        role = \"target arm\" if label == target else \"other arm\"\n",
+        "        rts = df.loc[df[\"response\"] == label, \"rt\"]\n",
+        "        ax.hist(\n",
+        "            rts,\n",
+        "            bins=45,\n",
+        "            alpha=0.5,\n",
+        "            label=f\"response {label} ({role}, n={len(rts)})\",\n",
+        "            color=color,\n",
+        "            density=True,\n",
+        "        )\n",
+        "\n",
+        "    ax.set_xlabel(\"Response time (s)\")\n",
+        "    ax.set_ylabel(\"Density\")\n",
+        "    ax.set_title(dataset[\"label\"])\n",
+        "    ax.legend()\n",
+        "    return ax\n",
+        "\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 2, figsize=(14, 4), sharey=True)\n",
+        "plot_rt_distributions(\"bernoulli\", ax=axes[0])\n",
+        "plot_rt_distributions(\"gaussian\", ax=axes[1])\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0a04125b",
+      "metadata": {},
+      "source": [
+        "## 7. HSSM Config Bridge\n",
+        "\n",
+        "`ModelConfig.to_hssm_config_dict()` exports the shared structural fields needed to complete an HSSM-side `RLSSMConfig`. Inference-only likelihood functions remain placeholders for HSSM code to fill."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d8280686",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "hssm_config = gaussian_config.to_hssm_config_dict()\n",
+        "important_keys = [\n",
+        "    \"model_name\",\n",
+        "    \"decision_process\",\n",
+        "    \"list_params\",\n",
+        "    \"choices\",\n",
+        "    \"response\",\n",
+        "    \"response_mapping\",\n",
+        "    \"extra_fields\",\n",
+        "    \"decision_process_loglik_kind\",\n",
+        "    \"learning_process_kind\",\n",
+        "]\n",
+        "\n",
+        "for key in important_keys:\n",
+        "    print(f\"{key}: {hssm_config[key]!r}\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "ssm-simulators (3.12.11)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/mkdocs.yml b/mkdocs.yml
index e9731381..4c533081 100755
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -14,6 +14,7 @@ nav:
       - Configuration Systems: core_tutorials/tutorial_configs.ipynb
       - Custom Models: core_tutorials/tutorial_custom_models.ipynb
       - Data Generators: core_tutorials/tutorial_data_generators.ipynb
+      - RLSSM Simulators: core_tutorials/rlssm_simulator_demo.ipynb
       - KDE Class: core_tutorials/kde_class.ipynb
       - PyDDM Integration: core_tutorials/tutorial_simulators_vs_pyddm.ipynb
       - Using MLflow: core_tutorials/using_mlflow.md
diff --git a/ssms/__init__.py b/ssms/__init__.py
index 3fe2412f..4fd57d39 100755
--- a/ssms/__init__.py
+++ b/ssms/__init__.py
@@ -19,6 +19,7 @@
 from . import config
 from . import support_utils
 from . import hssm_support
+from . import rl
 from .basic_simulators import Simulator, OMISSION_SENTINEL
 from .config import get_default_generator_config
 
@@ -30,6 +31,7 @@
     "config",
     "support_utils",
     "hssm_support",
+    "rl",
     "Simulator",
     "OMISSION_SENTINEL",
     "get_default_generator_config",
diff --git a/ssms/rl/__init__.py b/ssms/rl/__init__.py
new file mode 100644
index 00000000..1e2b2fe0
--- /dev/null
+++ b/ssms/rl/__init__.py
@@ -0,0 +1,7 @@
+"""RLSSM simulation framework for ssm-simulators."""
+
+from . import env, learning, preset
+from .config import ModelConfig
+from .simulator import Simulator
+
+__all__ = ["Simulator", "ModelConfig", "env", "learning", "preset"]
diff --git a/ssms/rl/config.py b/ssms/rl/config.py
new file mode 100644
index 00000000..c3ca4f7f
--- /dev/null
+++ b/ssms/rl/config.py
@@ -0,0 +1,374 @@
+"""ModelConfig — structural model specification for RLSSM simulation."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Literal, cast
+
+from ssms.config.model_config_builder import ModelConfigBuilder
+
+from .env import TaskConfig, TaskEnvironment
+from .learning import LearningProcess
+
+
+# Fields that to_hssm_config_dict() must emit — kept as a constant
+# so we can write contract tests against it.
+_HSSM_SHARED_FIELDS = (
+    "model_name",
+    "description",
+    "list_params",
+    "bounds",
+    "params_default",
+    "choices",
+    "decision_process",
+    "response",
+    "response_mapping",
+    "extra_fields",
+)
+
+
+@dataclass
+class ModelConfig:
+    """RLSSM model configuration for ssm-simulators.
+
+    Describes the *structural specification* of an RLSSM model:
+    which learning process, which decision process (SSM), and which task
+    environment. Concrete parameter values are NOT stored here — they
+    are passed as ``theta`` to ``Simulator.simulate()``.
+
+    Parameters
+    ----------
+    model_name : str
+        Unique identifier for this RLSSM model (e.g., "rlssm_angle_rw").
+    description : str
+        Human-readable model description.
+    decision_process : str
+        SSM model name in ssm-simulators registry (e.g., "angle", "ddm").
+        Must be resolvable via ``ModelConfigBuilder.from_model()``.
+    learning_process : LearningProcess
+        Instance of a class satisfying the ``LearningProcess`` protocol.
+    task_environment : TaskEnvironment | TaskConfig
+        Task environment instance or a ``TaskConfig`` to auto-build one.
+        If ``TaskConfig``, ``build_environment()`` is called in ``__post_init__``.
+    list_params : list[str] | None
+        All free parameter names (RL + fixed SSM), in order.
+        If None, auto-derived: ``learning_process.free_params`` + fixed SSM params.
+    bounds : dict[str, tuple[float, float]] | None
+        Parameter bounds. If None, auto-derived from learning_process.param_bounds
+        + SSM model config param_bounds.
+    params_default : list[float] | None
+        Default values in same order as list_params. If None, auto-derived.
+    choices : tuple[int, ...] | None
+        SSM response labels (e.g., (-1, 1)). If None, taken from task_environment.
+    response : list[str]
+        Response column names. Default ["rt", "response"].
+    response_mapping : Literal["auto"] | dict[int, int]
+        Mapping from SSM response labels to zero-based learning actions.
+        ``"auto"`` maps labels by ``task_environment.response_labels`` order.
+    include_action : bool
+        Whether simulator output includes the derived zero-based ``action`` column.
+        Default False.
+    extra_fields : list[str] | None
+        Extra data columns beyond response. Default: ["feedback"] + task_environment.extra_fields.
+    computed_param_mapping : dict[str, str] | None
+        Optional override for non-name-matching handshakes.
+        Maps learning process output name -> SSM param name.
+        E.g., {"drift": "v"} if learning process outputs "drift" but SSM expects "v".
+        Default: None (same-name linking).
+    ssm_kwargs : dict
+        Default kwargs for the underlying SSM simulator call.
+        Default: {"delta_t": 0.001, "max_t": 20.0}.
+    """
+
+    model_name: str
+    description: str
+    decision_process: str
+    learning_process: LearningProcess
+    task_environment: TaskEnvironment | TaskConfig
+
+    # Auto-derivable fields (None = derive from components)
+    list_params: list[str] | None = None
+    bounds: dict[str, tuple[float, float]] | None = None
+    params_default: list[float] | None = None
+    choices: tuple[int, ...] | None = None
+    response: list[str] = field(default_factory=lambda: ["rt", "response"])
+    response_mapping: Literal["auto"] | dict[int, int] = "auto"
+    include_action: bool = False
+    extra_fields: list[str] | None = None
+
+    # Optional handshake override
+    computed_param_mapping: dict[str, str] | None = None
+
+    # SSM simulator defaults
+    ssm_kwargs: dict[str, Any] = field(
+        default_factory=lambda: {"delta_t": 0.001, "max_t": 20.0}
+    )
+
+    def __post_init__(self):
+        """Auto-build task environment and derive missing fields."""
+        # Convert TaskConfig -> TaskEnvironment
+        if isinstance(self.task_environment, TaskConfig):
+            self.task_environment = self.task_environment.build_environment()
+
+        # Load SSM model config for validation and auto-derivation
+        self._ssm_config = ModelConfigBuilder.from_model(self.decision_process)
+
+        # Auto-derive fields if not provided
+        if self.choices is None:
+            self.choices = tuple(self.task_environment.response_labels)
+
+        self.response_to_action = self._normalize_response_mapping()
+        self._validate_ssm_choices()
+
+        if self.extra_fields is None:
+            base_extra = ["feedback"]
+            env_extra = self.task_environment.extra_fields or []
+            self.extra_fields = base_extra + [
+                f for f in env_extra if f not in base_extra
+            ]
+
+        # Resolve the handshake: which SSM params are computed vs fixed
+        self._resolve_handshake()
+
+        # Auto-derive list_params, bounds, params_default
+        if self.list_params is None:
+            self.list_params = self._derive_list_params()
+        if self.bounds is None:
+            self.bounds = self._derive_bounds()
+        if self.params_default is None:
+            self.params_default = self._derive_params_default()
+
+    def _normalize_response_mapping(self) -> dict[int, int]:
+        """Normalize response labels to zero-based action indices."""
+        task_environment = cast(TaskEnvironment, self.task_environment)
+        response_labels = list(task_environment.response_labels)
+        n_arms = task_environment.n_arms
+        if tuple(response_labels) != tuple(self.choices):
+            raise ValueError(
+                "choices must match task_environment.response_labels. "
+                f"Got choices={self.choices} and response_labels={response_labels}."
+            )
+
+        if self.response_mapping == "auto":
+            return {label: action for action, label in enumerate(response_labels)}
+
+        mapping = {
+            int(response): int(action)
+            for response, action in self.response_mapping.items()
+        }
+        label_set = set(response_labels)
+        mapping_labels = set(mapping)
+        if mapping_labels != label_set:
+            missing = sorted(label_set - mapping_labels)
+            extra = sorted(mapping_labels - label_set)
+            raise ValueError(
+                "response_mapping must cover response labels exactly. "
+                f"Missing: {missing}; extra: {extra}."
+            )
+
+        values = list(mapping.values())
+        expected_actions = set(range(n_arms))
+        action_set = set(values)
+        if len(action_set) != len(values):
+            raise ValueError("response_mapping action values must be unique")
+        if action_set != expected_actions:
+            raise ValueError(
+                "response_mapping action values must be exactly "
+                f"{sorted(expected_actions)}. Got {sorted(action_set)}."
+            )
+        return mapping
+
+    def _validate_ssm_choices(self) -> None:
+        """Ensure task response labels match the decision simulator labels."""
+        ssm_choices = self._ssm_config.get("choices")
+        if ssm_choices is None:
+            return
+        ssm_choices_tuple = tuple(int(choice) for choice in ssm_choices)
+        if tuple(self.choices) != ssm_choices_tuple:
+            raise ValueError(
+                "choices and task_environment.response_labels must match SSM choices "
+                f"for decision_process='{self.decision_process}'. "
+                f"Got choices={self.choices}; SSM choices={ssm_choices_tuple}."
+            )
+
+    def _resolve_handshake(self):
+        """Resolve which SSM params are computed by learning vs fixed by user.
+
+        Populates:
+        - self._computed_ssm_params: SSM param names filled by learning process
+        - self._fixed_ssm_params: SSM param names user must provide in theta
+        """
+        ssm_params: list[str] = list(self._ssm_config["params"])
+        learning_outputs = self.learning_process.computed_params
+
+        # Apply computed_param_mapping if provided
+        mapping = self.computed_param_mapping or {}
+        computed_ssm_params: list[str] = []
+        for output_name in learning_outputs:
+            ssm_name = mapping.get(output_name, output_name)
+            computed_ssm_params.append(ssm_name)
+
+        if len(set(computed_ssm_params)) != len(computed_ssm_params):
+            raise ValueError(
+                "computed_param_mapping must map learning outputs to unique "
+                f"SSM parameter names. Got mapped params: {computed_ssm_params}."
+            )
+
+        self._computed_ssm_params = computed_ssm_params
+        self._fixed_ssm_params: list[str] = [
+            p for p in ssm_params if p not in computed_ssm_params
+        ]
+
+    @property
+    def required_params(self) -> list[str]:
+        """Parameters that simulation requires from ``theta``."""
+        return list(self.learning_process.free_params) + list(self._fixed_ssm_params)
+
+    def _derive_list_params(self) -> list[str]:
+        """RL free params + fixed SSM params (in that order)."""
+        return list(self.learning_process.free_params) + self._fixed_ssm_params
+
+    def _derive_bounds(self) -> dict[str, tuple[float, float]]:
+        """Merge RL param bounds + SSM param bounds for fixed params."""
+        bounds = dict(self.learning_process.param_bounds)
+        ssm_bounds_dict = self._ssm_config.get("param_bounds_dict", {})
+        if not ssm_bounds_dict:
+            # Fallback: build from parallel arrays
+            ssm_param_names = self._ssm_config["params"]
+            ssm_lower = self._ssm_config["param_bounds"][0]
+            ssm_upper = self._ssm_config["param_bounds"][1]
+            ssm_bounds_dict = {
+                name: (float(lo), float(hi))
+                for name, lo, hi in zip(ssm_param_names, ssm_lower, ssm_upper)
+            }
+        for p in self._fixed_ssm_params:
+            bounds[p] = ssm_bounds_dict[p]
+        return bounds
+
+    def _derive_params_default(self) -> list[float]:
+        """Default values in list_params order."""
+        rl_defaults = self.learning_process.default_params
+        ssm_defaults_list = self._ssm_config["default_params"]
+        ssm_param_names = self._ssm_config["params"]
+        ssm_defaults = dict(zip(ssm_param_names, ssm_defaults_list))
+        defaults = []
+        for p in self.list_params:
+            if p in rl_defaults:
+                defaults.append(float(rl_defaults[p]))
+            elif p in ssm_defaults:
+                defaults.append(float(ssm_defaults[p]))
+            else:
+                raise ValueError(f"No default value for param '{p}'")
+        return defaults
+
+    def validate(self) -> None:
+        """Validate config consistency. Called by Simulator.__init__().
+
+        Checks:
+        1. decision_process exists in ssm-simulators registry
+        2. Handshake: computed + fixed params cover all SSM params exactly once
+        3. No param is both computed and fixed
+        4. list_params length matches params_default length
+        5. All list_params have bounds
+        """
+        ssm_params = set(self._ssm_config["params"])
+
+        # Handshake coverage
+        computed = set(self._computed_ssm_params)
+        fixed = set(self._fixed_ssm_params)
+        covered = computed | fixed
+        missing = ssm_params - covered
+        if missing:
+            raise ValueError(
+                f"SSM model '{self.decision_process}' requires params "
+                f"{sorted(ssm_params)}, but the following are neither computed "
+                f"by the learning process nor available as fixed params: "
+                f"{sorted(missing)}. Learning process computes: {sorted(computed)}. "
+                f"Fixed SSM params: {sorted(fixed)}."
+            )
+
+        # No overlap
+        overlap = computed & fixed
+        if overlap:
+            raise ValueError(
+                f"Params {sorted(overlap)} are both computed by the learning "
+                f"process and listed as fixed SSM params. Each param must have "
+                f"exactly one source."
+            )
+
+        # Unknown computed params
+        unknown_computed = computed - ssm_params
+        if unknown_computed:
+            raise ValueError(
+                f"Learning process computes {sorted(unknown_computed)} which "
+                f"are not params of the '{self.decision_process}' SSM model."
+            )
+
+        # list_params / params_default consistency
+        if self.list_params and self.params_default:
+            if len(self.list_params) != len(self.params_default):
+                raise ValueError(
+                    f"list_params length ({len(self.list_params)}) != "
+                    f"params_default length ({len(self.params_default)})"
+                )
+
+        # All list_params have bounds
+        if self.list_params and self.bounds:
+            missing_bounds = [p for p in self.list_params if p not in self.bounds]
+            if missing_bounds:
+                raise ValueError(f"Missing bounds for params: {missing_bounds}")
+
+        required_params = self.required_params
+        if self.list_params:
+            missing_required = sorted(set(required_params) - set(self.list_params))
+            extra_params = sorted(set(self.list_params) - set(required_params))
+            has_duplicates = len(set(self.list_params)) != len(self.list_params)
+            if missing_required or extra_params or has_duplicates:
+                raise ValueError(
+                    "list_params must match required params from the learning "
+                    "process and fixed SSM parameters. "
+                    f"Missing: {missing_required}; extra: {extra_params}; "
+                    f"required: {required_params}."
+                )
+
+        task_environment = cast(TaskEnvironment, self.task_environment)
+        learning_n_actions = getattr(self.learning_process, "n_actions", None)
+        if (
+            learning_n_actions is not None
+            and learning_n_actions != task_environment.n_arms
+        ):
+            raise ValueError(
+                "learning_process.n_actions must match task_environment.n_arms. "
+                f"Got n_actions={learning_n_actions}, n_arms={task_environment.n_arms}."
+            )
+
+    def to_hssm_config_dict(self) -> dict[str, Any]:
+        """Produce a dict compatible with HSSM's RLSSMConfig.from_rlssm_dict().
+
+        The output contains all fields from _HSSM_SHARED_FIELDS plus placeholder
+        values for inference-only fields that the user must fill in on the HSSM side.
+
+        Returns
+        -------
+        dict[str, Any]
+            Dict ready for ``RLSSMConfig.from_rlssm_dict(result)`` after user
+            fills in inference-only fields.
+        """
+        return {
+            # Shared structural fields
+            "model_name": self.model_name,
+            "description": self.description,
+            "decision_process": self.decision_process,
+            "list_params": list(self.list_params),
+            "bounds": dict(self.bounds),
+            "params_default": list(self.params_default),
+            "choices": tuple(self.choices),
+            "response": list(self.response),
+            "response_mapping": dict(self.response_to_action),
+            "extra_fields": list(self.extra_fields) if self.extra_fields else [],
+            # Inference-only placeholders (user fills on HSSM side)
+            "ssm_logp_func": None,
+            "learning_process": {},
+            "decision_process_loglik_kind": "approx_differentiable",
+            "learning_process_kind": "blackbox",
+        }
diff --git a/ssms/rl/env.py b/ssms/rl/env.py
new file mode 100644
index 00000000..5f371446
--- /dev/null
+++ b/ssms/rl/env.py
@@ -0,0 +1,253 @@
+"""TaskEnvironment protocol and built-in implementations."""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import Protocol, runtime_checkable
+
+import numpy as np
+
+
+@runtime_checkable
+class TaskEnvironment(Protocol):
+    """Protocol for RLSSM task environments.
+
+    A task environment generates rewards and optional per-trial context data.
+    It is stateful and must be reset before each participant.
+    """
+
+    @property
+    def n_arms(self) -> int:
+        """Number of available zero-based learning actions."""
+        ...
+
+    @property
+    def response_labels(self) -> list[int]:
+        """SSM response labels corresponding to actions in order."""
+        ...
+
+    @property
+    def extra_fields(self) -> list[str]:
+        """Names of additional per-trial data columns this environment provides."""
+        ...
+
+    def reset(self, rng: np.random.Generator | None = None) -> None:
+        """Reset environment state for a new participant."""
+        ...
+
+    def sample_reward(self, action: int, trial_idx: int) -> float:
+        """Sample reward for a zero-based learning action on the given trial."""
+        ...
+
+    def get_extra_data(self, trial_idx: int) -> dict[str, float]:
+        """Return additional per-trial data columns."""
+        ...
+
+
+class _RewardDistribution(Protocol):
+    @property
+    def n_arms(self) -> int: ...
+
+    def sample(self, action: int, rng: np.random.Generator) -> float: ...
+
+
+class _BernoulliRewards:
+    def __init__(self, probabilities: list[float]):
+        if len(probabilities) < 2:
+            raise ValueError("Bandit environments require at least 2 arms")
+        for probability in probabilities:
+            if not 0.0 <= probability <= 1.0:
+                raise ValueError(f"Reward probability {probability} not in [0, 1]")
+        self._probabilities = list(probabilities)
+
+    @property
+    def n_arms(self) -> int:
+        return len(self._probabilities)
+
+    def sample(self, action: int, rng: np.random.Generator) -> float:
+        return float(rng.random() < self._probabilities[action])
+
+
+class _GaussianRewards:
+    def __init__(self, means: list[float], sds: list[float]):
+        if len(means) < 2:
+            raise ValueError("Bandit environments require at least 2 arms")
+        if len(means) != len(sds):
+            raise ValueError(
+                f"means length ({len(means)}) must match sds length ({len(sds)})"
+            )
+        for sd in sds:
+            if sd <= 0.0:
+                raise ValueError(f"Reward standard deviation {sd} must be positive")
+        self._means = list(means)
+        self._sds = list(sds)
+
+    @property
+    def n_arms(self) -> int:
+        return len(self._means)
+
+    def sample(self, action: int, rng: np.random.Generator) -> float:
+        return float(rng.normal(self._means[action], self._sds[action]))
+
+
+class Bandit:
+    """Generic bandit task environment.
+
+    Public constructors are ``Bandit.bernoulli(...)`` and
+    ``Bandit.gaussian(...)``. Rewards are sampled by zero-based action index;
+    ``response_labels`` define the SSM labels mapped onto those actions.
+    """
+
+    def __init__(
+        self,
+        rewards: _RewardDistribution,
+        response_labels: list[int] | None = None,
+    ):
+        self._rewards = rewards
+        self._response_labels = self._validate_response_labels(
+            response_labels, rewards.n_arms
+        )
+        self._rng: np.random.Generator | None = None
+
+    @classmethod
+    def bernoulli(
+        cls,
+        probabilities: list[float] | None = None,
+        response_labels: list[int] | None = None,
+    ) -> Bandit:
+        """Build a Bernoulli-reward bandit."""
+        if probabilities is None:
+            probabilities = [0.7, 0.3]
+        return cls(
+            rewards=_BernoulliRewards(probabilities),
+            response_labels=response_labels,
+        )
+
+    @classmethod
+    def gaussian(
+        cls,
+        means: list[float] | None = None,
+        sds: list[float] | None = None,
+        response_labels: list[int] | None = None,
+    ) -> Bandit:
+        """Build a Gaussian-reward bandit."""
+        if means is None:
+            means = [1.0, 0.0]
+        if sds is None:
+            sds = [1.0] * len(means)
+        return cls(
+            rewards=_GaussianRewards(means, sds), response_labels=response_labels
+        )
+
+    @staticmethod
+    def _validate_response_labels(
+        response_labels: list[int] | None, n_arms: int
+    ) -> list[int]:
+        if n_arms < 2:
+            raise ValueError("Bandit environments require at least 2 arms")
+        labels = (
+            list(range(n_arms)) if response_labels is None else list(response_labels)
+        )
+        if len(labels) != n_arms:
+            raise ValueError(
+                f"response_labels length ({len(labels)}) must match n_arms ({n_arms})"
+            )
+        if len(set(labels)) != len(labels):
+            raise ValueError("response_labels must be unique")
+        return labels
+
+    @property
+    def n_arms(self) -> int:
+        return self._rewards.n_arms
+
+    @property
+    def response_labels(self) -> list[int]:
+        return list(self._response_labels)
+
+    @property
+    def extra_fields(self) -> list[str]:
+        return []
+
+    def reset(self, rng: np.random.Generator | None = None) -> None:
+        self._rng = rng or np.random.default_rng()
+
+    def sample_reward(self, action: int, trial_idx: int) -> float:
+        if self._rng is None:
+            raise RuntimeError("Call reset() before sample_reward()")
+        if action < 0 or action >= self.n_arms:
+            raise ValueError(
+                f"Action {action} is out of range for bandit with {self.n_arms} arms"
+            )
+        return self._rewards.sample(action, self._rng)
+
+    def get_extra_data(self, trial_idx: int) -> dict[str, float]:
+        return {}
+
+
+TaskEnvironmentBuilder = Callable[[str | None, dict], TaskEnvironment]
+_TASK_REGISTRY: dict[str, TaskEnvironmentBuilder] = {}
+
+
+def register_task(task: str, builder: TaskEnvironmentBuilder) -> None:
+    """Register a task environment builder for ``TaskConfig``."""
+    _TASK_REGISTRY[task] = builder
+
+
+def registered_tasks() -> list[str]:
+    """List task names available through ``TaskConfig``."""
+    return sorted(_TASK_REGISTRY)
+
+
+class TaskConfig:
+    """Convenience configuration for registered task environments.
+
+    ``TaskConfig`` is a shorthand that delegates task-specific options to a
+    registry builder. Built in support currently includes ``task="bandit"``
+    with ``reward="bernoulli"`` or ``reward="gaussian"``.
+    """
+
+    def __init__(self, task: str = "bandit", reward: str | None = None, **options):
+        self.task = task
+        self.reward = reward
+        self.options = dict(options)
+
+    def build_environment(self) -> TaskEnvironment:
+        if self.task not in _TASK_REGISTRY:
+            available = registered_tasks()
+            raise ValueError(
+                f"Unknown task '{self.task}'. Registered tasks: {available}."
+            )
+        return _TASK_REGISTRY[self.task](self.reward, dict(self.options))
+
+
+def _build_bandit(reward: str | None, options: dict) -> TaskEnvironment:
+    reward = reward or "bernoulli"
+    if reward == "bernoulli":
+        allowed = {"probabilities", "response_labels"}
+        _validate_options("bandit", reward, options, allowed)
+        return Bandit.bernoulli(
+            probabilities=options.get("probabilities"),
+            response_labels=options.get("response_labels"),
+        )
+    if reward == "gaussian":
+        allowed = {"means", "sds", "response_labels"}
+        _validate_options("bandit", reward, options, allowed)
+        return Bandit.gaussian(
+            means=options.get("means"),
+            sds=options.get("sds"),
+            response_labels=options.get("response_labels"),
+        )
+    raise ValueError(
+        f"Unknown bandit reward '{reward}'. Supported rewards: 'bernoulli', 'gaussian'."
+    )
+
+
+def _validate_options(task: str, reward: str, options: dict, allowed: set[str]) -> None:
+    unknown = sorted(set(options) - allowed)
+    if unknown:
+        raise TypeError(
+            f"Unsupported options for task='{task}', reward='{reward}': {unknown}"
+        )
+
+
+register_task("bandit", _build_bandit)
diff --git a/ssms/rl/learning.py b/ssms/rl/learning.py
new file mode 100644
index 00000000..aa3a903b
--- /dev/null
+++ b/ssms/rl/learning.py
@@ -0,0 +1,176 @@
+"""LearningProcess protocol and built-in implementations."""
+
+from __future__ import annotations
+
+from typing import Protocol, runtime_checkable
+
+import numpy as np
+
+
+@runtime_checkable
+class LearningProcess(Protocol):
+    """Protocol for RLSSM learning processes.
+
+    A learning process maintains internal state (e.g., Q-values) and computes
+    SSM parameters (e.g., drift rate) from that state on each trial. After each
+    trial's decision and reward, the state is updated.
+
+    The ``computed_params`` property is the formal HANDSHAKE between the learning
+    process and the decision process — it declares which SSM parameters the
+    learning process produces. The simulator validates that these, together with
+    fixed SSM params provided by the user, cover all parameters required by the
+    decision process model.
+    """
+
+    @property
+    def computed_params(self) -> list[str]:
+        """SSM parameter names this process computes (e.g., ['v']).
+
+        This is the handshake — declares what SSM parameters are
+        informed by the learning process."""
+        ...
+
+    @property
+    def free_params(self) -> list[str]:
+        """RL parameter names this process requires from theta
+        (e.g., ['rl_alpha', 'scaler'])."""
+        ...
+
+    @property
+    def param_bounds(self) -> dict[str, tuple[float, float]]:
+        """Bounds for each free param. Used by config validation
+        and to_hssm_config_dict()."""
+        ...
+
+    @property
+    def default_params(self) -> dict[str, float]:
+        """Default values for each free param."""
+        ...
+
+    def reset(self, **kwargs) -> None:
+        """Reset internal state for a new participant.
+        Called at the start of each participant's trial sequence."""
+        ...
+
+    def compute_ssm_params(self, trial_params: dict[str, float]) -> dict[str, float]:
+        """Compute SSM parameters from current learning state.
+        Called BEFORE the SSM runs on each trial.
+        ``trial_params`` contains the RL free params for this trial.
+        Returns e.g. {'v': 0.35}."""
+        ...
+
+    def update(
+        self, action: int, reward: float, trial_params: dict[str, float]
+    ) -> None:
+        """Update learning state given the choice outcome.
+        Called AFTER the SSM runs and reward is generated.
+        ``action`` is the zero-based learning action index."""
+        ...
+
+
+class RescorlaWagnerDeltaRule:
+    """Rescorla-Wagner delta learning rule for 2-armed bandit tasks.
+
+    Computes drift rate as scaled Q-value difference: v = (Q[1] - Q[0]) * scaler.
+    Updates Q-values via: Q[action] += alpha * (reward - Q[action]).
+
+    Numerically equivalent to HSSM's compute_v_trial_wise() in
+    src/hssm/rl/likelihoods/two_armed_bandit.py.
+
+    Parameters
+    ----------
+    n_actions : int
+        Number of choice alternatives. Default 2.
+    initial_q : float
+        Initial Q-value for all alternatives. Default 0.5.
+        Matches HSSM's ``jnp.ones(2) * 0.5``.
+    """
+
+    def __init__(self, n_actions: int = 2, initial_q: float = 0.5):
+        if n_actions != 2:
+            raise ValueError(
+                "n_actions must be 2; RescorlaWagnerDeltaRule supports "
+                "two-action tasks only"
+            )
+        self._n_actions = n_actions
+        self._initial_q = initial_q
+        self._q_values: np.ndarray | None = None
+
+    @property
+    def n_actions(self) -> int:
+        return self._n_actions
+
+    @property
+    def computed_params(self) -> list[str]:
+        return ["v"]
+
+    @property
+    def free_params(self) -> list[str]:
+        return ["rl_alpha", "scaler"]
+
+    @property
+    def param_bounds(self) -> dict[str, tuple[float, float]]:
+        return {"rl_alpha": (0.0, 1.0), "scaler": (0.001, 10.0)}
+
+    @property
+    def default_params(self) -> dict[str, float]:
+        return {"rl_alpha": 0.2, "scaler": 2.0}
+
+    @property
+    def q_values(self) -> np.ndarray | None:
+        """Current Q-values. None if reset() has not been called."""
+        return self._q_values.copy() if self._q_values is not None else None
+
+    def reset(self, **kwargs) -> None:
+        self._q_values = np.full(self._n_actions, self._initial_q, dtype=np.float64)
+
+    def compute_ssm_params(self, trial_params: dict[str, float]) -> dict[str, float]:
+        """Drift = (Q[1] - Q[0]) * scaler.
+
+        NOTE: drift is computed BEFORE the Q-value update for this trial,
+        matching HSSM's scan order where computed_v precedes delta_RL update.
+        """
+        scaler = trial_params["scaler"]
+        v = float((self._q_values[1] - self._q_values[0]) * scaler)
+        return {"v": v}
+
+    def update(
+        self, action: int, reward: float, trial_params: dict[str, float]
+    ) -> None:
+        """Q[action] += alpha * (reward - Q[action])."""
+        alpha = trial_params["rl_alpha"]
+        delta = reward - self._q_values[action]
+        self._q_values[action] += alpha * delta
+
+
+class RescorlaWagnerDualAlphaRule(RescorlaWagnerDeltaRule):
+    """Rescorla-Wagner delta learning rule with separate learning rates.
+
+    Positive prediction errors use ``rl_alpha`` and negative prediction errors
+    use ``rl_alpha_neg``. Drift computation and Q-value initialization match
+    ``RescorlaWagnerDeltaRule``.
+    """
+
+    @property
+    def free_params(self) -> list[str]:
+        return ["rl_alpha", "rl_alpha_neg", "scaler"]
+
+    @property
+    def param_bounds(self) -> dict[str, tuple[float, float]]:
+        return {
+            "rl_alpha": (0.0, 1.0),
+            "rl_alpha_neg": (0.0, 1.0),
+            "scaler": (0.001, 10.0),
+        }
+
+    @property
+    def default_params(self) -> dict[str, float]:
+        return {"rl_alpha": 0.2, "rl_alpha_neg": 0.2, "scaler": 2.0}
+
+    def update(
+        self, action: int, reward: float, trial_params: dict[str, float]
+    ) -> None:
+        """Update Q[action] with sign-dependent learning rates."""
+        delta = reward - self._q_values[action]
+        alpha = trial_params["rl_alpha_neg"] if delta < 0 else trial_params["rl_alpha"]
+        self._q_values[action] += alpha * delta
diff --git a/ssms/rl/preset.py b/ssms/rl/preset.py
new file mode 100644
index 00000000..1308473e
--- /dev/null
+++ b/ssms/rl/preset.py
@@ -0,0 +1,52 @@
+"""Registry for RLSSM presets."""
+
+from __future__ import annotations
+
+import builtins
+from typing import Callable
+
+from .config import ModelConfig
+
+_PRESETS: dict[str, Callable[[], ModelConfig]] = {}
+
+
+def register(name: str, factory: Callable[[], ModelConfig]) -> None:
+    """Register a named RLSSM preset."""
+    _PRESETS[name] = factory
+
+
+def get(name: str) -> ModelConfig:
+    """Get a named RLSSM preset config. Returns a fresh instance."""
+    if name not in _PRESETS:
+        available = sorted(_PRESETS.keys())
+        raise KeyError(f"Unknown RLSSM preset '{name}'. Available: {available}")
+    return _PRESETS[name]()
+
+
+def list() -> builtins.list[str]:
+    """List available RLSSM preset names."""
+    return sorted(_PRESETS.keys())
+
+
+# --- v1 Built-in Presets ---
+
+
+def _make_rlssm1() -> ModelConfig:
+    from .env import Bandit
+    from .learning import RescorlaWagnerDeltaRule
+
+    return ModelConfig(
+        model_name="rlssm1",
+        description=(
+            "RLSSM: Rescorla-Wagner delta rule + angle SSM + "
+            "two-armed Bernoulli bandit. Matches HSSM's rlssm1 preset."
+        ),
+        decision_process="angle",
+        learning_process=RescorlaWagnerDeltaRule(n_actions=2, initial_q=0.5),
+        task_environment=Bandit.bernoulli(
+            probabilities=[0.7, 0.3], response_labels=[-1, 1]
+        ),
+    )
+
+
+register("rlssm1", _make_rlssm1)
diff --git a/ssms/rl/simulator.py b/ssms/rl/simulator.py
new file mode 100644
index 00000000..3c203810
--- /dev/null
+++ b/ssms/rl/simulator.py
@@ -0,0 +1,190 @@
+"""Simulator — interleaved learning + SSM decision simulation."""
+
+from __future__ import annotations
+
+from typing import cast
+
+import numpy as np
+import pandas as pd
+
+from ssms.basic_simulators import OMISSION_SENTINEL
+from ssms.basic_simulators.simulator import simulator as ssm_simulator
+
+from .config import ModelConfig
+from .env import TaskEnvironment
+
+
+MISSING_RESPONSE_SENTINEL = -999
+
+
+class Simulator:
+    """RLSSM simulator composing a learning process with an SSM decision process.
+
+    Runs the interleaved trial-by-trial loop:
+    compute SSM params -> simulate SSM -> observe choice -> generate reward -> update learning.
+
+    Reuses the existing ssm-simulators ``simulator()`` function with ``n_samples=1``
+    for each trial. No Cython modifications needed — all 40+ SSM models work as
+    decision processes out of the box.
+
+    Parameters
+    ----------
+    config : ModelConfig
+        Structural model configuration. Validated on construction.
+    """
+
+    def __init__(self, config: ModelConfig):
+        self.config = config
+        config.validate()
+
+    def simulate(
+        self,
+        theta: dict[str, float],
+        n_trials: int = 200,
+        n_participants: int = 20,
+        random_state: int | None = None,
+    ) -> pd.DataFrame:
+        """Run full RLSSM simulation.
+
+        Parameters
+        ----------
+        theta : dict[str, float]
+            Concrete parameter values. Must contain all params required by the
+            learning process and fixed SSM parameters.
+        n_trials : int
+            Number of trials per participant. Default 200.
+        n_participants : int
+            Number of participants to simulate. Default 20.
+        random_state : int | None
+            Seed for reproducibility. If None, non-deterministic.
+
+        Returns
+        -------
+        pd.DataFrame
+            Balanced panel with columns: participant_id, trial_id, rt, response,
+            feedback, plus any extra_fields from the task environment.
+        """
+        self._validate_theta(theta)
+
+        rng = np.random.default_rng(random_state)
+        child_rngs = rng.spawn(n_participants)
+
+        all_rows = []
+        for p in range(n_participants):
+            rows = self._simulate_subject(p, theta, n_trials, child_rngs[p])
+            all_rows.extend(rows)
+
+        df = pd.DataFrame(all_rows)
+        df = df.sort_values(["participant_id", "trial_id"]).reset_index(drop=True)
+        return df
+
+    def _validate_theta(self, theta: dict[str, float]) -> None:
+        """Check that theta contains all required params."""
+        required_params = self.config.required_params
+        missing = [p for p in required_params if p not in theta]
+        if missing:
+            raise ValueError(
+                f"theta is missing required params: {missing}. "
+                f"Expected all of: {required_params}"
+            )
+
+    def _simulate_subject(
+        self,
+        subject_id: int,
+        theta: dict[str, float],
+        n_trials: int,
+        rng: np.random.Generator,
+    ) -> list[dict]:
+        """Simulate one participant's trial sequence."""
+        config = self.config
+        lp = config.learning_process
+        env = cast(TaskEnvironment, config.task_environment)
+
+        # Split theta into RL params and fixed SSM params
+        rl_params = {k: theta[k] for k in lp.free_params}
+        fixed_ssm_params = {k: theta[k] for k in config._fixed_ssm_params}
+
+        # Build the computed_param_mapping (learning output -> SSM param name)
+        mapping = config.computed_param_mapping or {}
+
+        # Reset learning process and task environment
+        lp.reset()
+        env.reset(rng=rng)
+
+        rows = []
+        for t in range(n_trials):
+            # COMPUTE: learning process produces SSM params from current state
+            computed_raw = lp.compute_ssm_params(rl_params)
+
+            # Apply mapping: learning output name -> SSM param name
+            computed_ssm = {}
+            for output_name, value in computed_raw.items():
+                ssm_name = mapping.get(output_name, output_name)
+                computed_ssm[ssm_name] = value
+
+            # MERGE: fixed SSM params + computed SSM params
+            full_theta = {**fixed_ssm_params, **computed_ssm}
+
+            # SIMULATE: one SSM trial
+            trial_seed = int(rng.integers(0, 2**31))
+            result = ssm_simulator(
+                theta=full_theta,
+                model=config.decision_process,
+                n_samples=1,
+                random_state=trial_seed,
+                **config.ssm_kwargs,
+            )
+
+            rt = float(result["rts"].item())
+            ssm_choice = int(result["choices"].item())
+
+            # OMISSION CHECK
+            if rt == OMISSION_SENTINEL:
+                row = {
+                    "participant_id": subject_id,
+                    "trial_id": t,
+                    "rt": OMISSION_SENTINEL,
+                    "response": MISSING_RESPONSE_SENTINEL,
+                    "feedback": 0.0,
+                }
+                if config.include_action:
+                    row["action"] = MISSING_RESPONSE_SENTINEL
+                row.update(env.get_extra_data(t))
+                rows.append(row)
+                continue
+
+            # Use the SSM choice label as the recorded response, but convert it
+            # to a zero-based action index for the task environment and learning rule.
+            response = ssm_choice
+            action = self._response_to_action_index(response)
+
+            # REWARD
+            reward = env.sample_reward(action, t)
+
+            # UPDATE learning process
+            lp.update(action, reward, rl_params)
+
+            # RECORD
+            row = {
+                "participant_id": subject_id,
+                "trial_id": t,
+                "rt": rt,
+                "response": response,
+                "feedback": reward,
+            }
+            if config.include_action:
+                row["action"] = action
+            row.update(env.get_extra_data(t))
+            rows.append(row)
+
+        return rows
+
+    def _response_to_action_index(self, response: int) -> int:
+        """Map an SSM response label to the learning process action index."""
+        response_to_action = self.config.response_to_action
+        if response not in response_to_action:
+            raise ValueError(
+                f"SSM response {response} is not in response_mapping. "
+                f"Expected one of: {sorted(response_to_action)}."
+            )
+        return int(response_to_action[response])
diff --git a/tests/rl/__init__.py b/tests/rl/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/rl/test_hssm_compatibility.py b/tests/rl/test_hssm_compatibility.py
new file mode 100644
index 00000000..ada7e634
--- /dev/null
+++ b/tests/rl/test_hssm_compatibility.py
@@ -0,0 +1,160 @@
+"""Contract tests verifying output is consumable by HSSM."""
+
+import pandas as pd
+import pytest
+
+import ssms.rl as rl
+from ssms.rl.config import _HSSM_SHARED_FIELDS
+
+
+@pytest.fixture()
+def sim_data():
+    config = rl.ModelConfig(
+        model_name="test_compat",
+        description="Compatibility test",
+        decision_process="angle",
+        learning_process=rl.learning.RescorlaWagnerDeltaRule(),
+        task_environment=rl.env.Bandit.bernoulli(
+            probabilities=[0.7, 0.3], response_labels=[-1, 1]
+        ),
+    )
+    sim = rl.Simulator(config)
+    data = sim.simulate(
+        theta={
+            "rl_alpha": 0.2,
+            "scaler": 2.0,
+            "a": 1.5,
+            "z": 0.5,
+            "t": 0.3,
+            "theta": 0.2,
+        },
+        n_trials=20,
+        n_participants=3,
+        random_state=42,
+    )
+    return data, config
+
+
+class TestOutputDtypes:
+    def test_participant_id_int(self, sim_data):
+        data, _ = sim_data
+        assert pd.api.types.is_integer_dtype(data["participant_id"])
+
+    def test_trial_id_int(self, sim_data):
+        data, _ = sim_data
+        assert pd.api.types.is_integer_dtype(data["trial_id"])
+
+    def test_rt_float(self, sim_data):
+        data, _ = sim_data
+        assert pd.api.types.is_float_dtype(data["rt"])
+
+    def test_response_int(self, sim_data):
+        data, _ = sim_data
+        assert pd.api.types.is_integer_dtype(data["response"])
+
+    def test_feedback_float(self, sim_data):
+        data, _ = sim_data
+        assert pd.api.types.is_float_dtype(data["feedback"])
+
+
+class TestOutputQuality:
+    def test_no_nans(self, sim_data):
+        data, _ = sim_data
+        assert not data.isna().any().any()
+
+
+class TestToHssmConfigDictSchema:
+    def test_all_required_fields(self, sim_data):
+        _, config = sim_data
+        d = config.to_hssm_config_dict()
+        for field_name in _HSSM_SHARED_FIELDS:
+            assert field_name in d
+            assert d[field_name] is not None
+
+    def test_field_types(self, sim_data):
+        _, config = sim_data
+        d = config.to_hssm_config_dict()
+        assert isinstance(d["model_name"], str)
+        assert isinstance(d["description"], str)
+        assert isinstance(d["decision_process"], str)
+        assert isinstance(d["list_params"], list)
+        assert isinstance(d["bounds"], dict)
+        assert isinstance(d["params_default"], list)
+        assert isinstance(d["choices"], tuple)
+        assert isinstance(d["response"], list)
+        assert isinstance(d["response_mapping"], dict)
+        assert isinstance(d["extra_fields"], list)
+
+    def test_inference_placeholders_present(self, sim_data):
+        _, config = sim_data
+        d = config.to_hssm_config_dict()
+        assert "ssm_logp_func" in d
+        assert "learning_process" in d
+        assert "learning_process_kind" in d
+        assert "learning_process_loglik_kind" not in d
+
+
+class TestRegistry:
+    def test_list_presets(self):
+        presets = rl.preset.list()
+        assert "rlssm1" in presets
+
+    def test_get_rlssm1_preset(self):
+        config = rl.preset.get("rlssm1")
+        assert isinstance(config, rl.ModelConfig)
+        assert config.model_name == "rlssm1"
+        assert config.decision_process == "angle"
+
+    def test_rlssm1_preset_simulates(self):
+        config = rl.preset.get("rlssm1")
+        sim = rl.Simulator(config)
+        data = sim.simulate(
+            theta={
+                "rl_alpha": 0.2,
+                "scaler": 2.0,
+                "a": 1.5,
+                "z": 0.5,
+                "t": 0.3,
+                "theta": 0.2,
+            },
+            n_trials=10,
+            n_participants=2,
+            random_state=42,
+        )
+        assert len(data) == 20
+
+    def test_unknown_preset_raises(self):
+        with pytest.raises(KeyError, match="Unknown RLSSM preset"):
+            rl.preset.get("nonexistent")
+
+    def test_register_custom_preset(self):
+        def factory():
+            return rl.ModelConfig(
+                model_name="custom",
+                description="Custom preset",
+                decision_process="angle",
+                learning_process=rl.learning.RescorlaWagnerDeltaRule(),
+                task_environment=rl.env.Bandit.bernoulli(response_labels=[-1, 1]),
+            )
+
+        rl.preset.register("custom_test", factory)
+        assert "custom_test" in rl.preset.list()
+        assert rl.preset.get("custom_test").model_name == "custom"
+
+
+class TestPublicApiSurface:
+    def test_public_exports_are_small(self):
+        assert rl.__all__ == ["Simulator", "ModelConfig", "env", "learning", "preset"]
+
+    @pytest.mark.parametrize(
+        "name",
+        [
+            "RLSSMSimulator",
+            "RLSSMModelConfig",
+            "get_rlssm_preset",
+            "list_rlssm_presets",
+            "register_rlssm_preset",
+        ],
+    )
+    def test_old_developmental_names_are_not_exported(self, name):
+        assert not hasattr(rl, name)
diff --git a/tests/rl/test_learning_process.py b/tests/rl/test_learning_process.py
new file mode 100644
index 00000000..bdf00599
--- /dev/null
+++ b/tests/rl/test_learning_process.py
@@ -0,0 +1,223 @@
+"""Tests for LearningProcess protocol and Rescorla-Wagner learning rules."""
+
+import numpy as np
+import pytest
+
+from ssms.rl.learning import (
+    LearningProcess,
+    RescorlaWagnerDeltaRule,
+    RescorlaWagnerDualAlphaRule,
+)
+
+
+class TestRescorlaWagnerDeltaRule:
+    def setup_method(self):
+        self.rw = RescorlaWagnerDeltaRule(n_actions=2, initial_q=0.5)
+        self.rw.reset()
+
+    def test_initial_q_values(self):
+        np.testing.assert_array_equal(self.rw.q_values, [0.5, 0.5])
+
+    def test_compute_v_initial(self):
+        """With equal Q-values, v should be 0.0 regardless of scaler."""
+        for scaler in [0.5, 1.0, 2.0, 10.0]:
+            result = self.rw.compute_ssm_params({"scaler": scaler})
+            assert result["v"] == 0.0
+
+    def test_compute_v_after_update(self):
+        """After one update (action=0, reward=1.0, alpha=0.5):
+        Q = [0.5 + 0.5*(1.0 - 0.5), 0.5] = [0.75, 0.5]
+        v = (0.5 - 0.75) * 2.0 = -0.5
+        """
+        params = {"rl_alpha": 0.5, "scaler": 2.0}
+        self.rw.update(action=0, reward=1.0, trial_params=params)
+        np.testing.assert_allclose(self.rw.q_values, [0.75, 0.5])
+        result = self.rw.compute_ssm_params(params)
+        assert result["v"] == pytest.approx(-0.5)
+
+    def test_drift_before_update_ordering(self):
+        """compute_ssm_params returns drift BEFORE the update
+        (matching HSSM's scan ordering)."""
+        params = {"rl_alpha": 0.5, "scaler": 1.0}
+        # Before any update, drift should be 0
+        v_before = self.rw.compute_ssm_params(params)["v"]
+        assert v_before == 0.0
+        # Update action=1, reward=1.0 → Q=[0.5, 0.75]
+        self.rw.update(action=1, reward=1.0, trial_params=params)
+        # Now drift should reflect the updated Q-values
+        v_after = self.rw.compute_ssm_params(params)["v"]
+        assert v_after == pytest.approx(0.25)  # (0.75 - 0.5) * 1.0
+
+    def test_multiple_updates_trajectory(self):
+        """Run a fixed sequence and compare against hand-computed values."""
+        params = {"rl_alpha": 0.3, "scaler": 2.0}
+        # Trial sequence: (action, reward)
+        sequence = [(0, 1.0), (1, 0.0), (0, 1.0), (1, 1.0)]
+
+        expected_q = [
+            # After (0, 1.0): Q[0] = 0.5 + 0.3*(1.0-0.5) = 0.65, Q[1] = 0.5
+            [0.65, 0.5],
+            # After (1, 0.0): Q[0] = 0.65, Q[1] = 0.5 + 0.3*(0.0-0.5) = 0.35
+            [0.65, 0.35],
+            # After (0, 1.0): Q[0] = 0.65 + 0.3*(1.0-0.65) = 0.755, Q[1] = 0.35
+            [0.755, 0.35],
+            # After (1, 1.0): Q[0] = 0.755, Q[1] = 0.35 + 0.3*(1.0-0.35) = 0.545
+            [0.755, 0.545],
+        ]
+
+        # Drift BEFORE each update (computed from Q before the trial's update)
+        expected_v_before = [
+            (0.5 - 0.5) * 2.0,  # 0.0
+            (0.5 - 0.65) * 2.0,  # -0.3
+            (0.35 - 0.65) * 2.0,  # -0.6
+            (0.35 - 0.755) * 2.0,  # -0.81
+        ]
+
+        for i, (action, reward) in enumerate(sequence):
+            v = self.rw.compute_ssm_params(params)["v"]
+            assert v == pytest.approx(expected_v_before[i], abs=1e-12)
+            self.rw.update(action=action, reward=reward, trial_params=params)
+            np.testing.assert_allclose(self.rw.q_values, expected_q[i], atol=1e-12)
+
+    def test_numerical_equivalence_with_hssm(self):
+        """Run same action/reward sequence through our RW and a NumPy
+        reimplementation of HSSM's compute_v_trial_wise. Assert match."""
+        alpha = 0.25
+        scaler = 1.5
+        params = {"rl_alpha": alpha, "scaler": scaler}
+
+        # Fixed action/reward sequence
+        actions = [0, 1, 1, 0, 1, 0, 0, 1, 0, 1]
+        rewards = [1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0]
+
+        # Our implementation
+        our_drifts = []
+        for action, reward in zip(actions, rewards):
+            v = self.rw.compute_ssm_params(params)["v"]
+            our_drifts.append(v)
+            self.rw.update(action=action, reward=reward, trial_params=params)
+
+        # HSSM-equivalent NumPy reimplementation
+        q_val = np.array([0.5, 0.5], dtype=np.float64)
+        hssm_drifts = []
+        for action, reward in zip(actions, rewards):
+            computed_v = (q_val[1] - q_val[0]) * scaler
+            hssm_drifts.append(float(computed_v))
+            delta_rl = reward - q_val[action]
+            q_val[action] = q_val[action] + alpha * delta_rl
+
+        np.testing.assert_allclose(our_drifts, hssm_drifts, atol=1e-15)
+
+    def test_protocol_compliance(self):
+        assert isinstance(RescorlaWagnerDeltaRule(), LearningProcess)
+
+    def test_reset_clears_state(self):
+        params = {"rl_alpha": 0.5, "scaler": 1.0}
+        self.rw.update(action=0, reward=1.0, trial_params=params)
+        assert not np.array_equal(self.rw.q_values, [0.5, 0.5])
+        self.rw.reset()
+        np.testing.assert_array_equal(self.rw.q_values, [0.5, 0.5])
+
+    def test_q_values_property_returns_copy(self):
+        q = self.rw.q_values
+        q[0] = 999.0
+        np.testing.assert_array_equal(self.rw.q_values, [0.5, 0.5])
+
+    def test_q_values_none_before_reset(self):
+        rw = RescorlaWagnerDeltaRule()
+        assert rw.q_values is None
+
+    def test_free_params(self):
+        assert self.rw.free_params == ["rl_alpha", "scaler"]
+
+    def test_param_bounds(self):
+        bounds = self.rw.param_bounds
+        assert bounds["rl_alpha"] == (0.0, 1.0)
+        assert bounds["scaler"] == (0.001, 10.0)
+
+    def test_default_params(self):
+        defaults = self.rw.default_params
+        assert defaults == {"rl_alpha": 0.2, "scaler": 2.0}
+
+    def test_invalid_n_actions(self):
+        with pytest.raises(ValueError, match="n_actions"):
+            RescorlaWagnerDeltaRule(n_actions=1)
+
+    def test_rejects_more_than_two_actions(self):
+        with pytest.raises(ValueError, match="two-action"):
+            RescorlaWagnerDeltaRule(n_actions=3)
+
+
+class TestRescorlaWagnerDualAlphaRule:
+    def setup_method(self):
+        self.rw = RescorlaWagnerDualAlphaRule(n_actions=2, initial_q=0.5)
+        self.rw.reset()
+
+    def test_protocol_compliance(self):
+        assert isinstance(RescorlaWagnerDualAlphaRule(), LearningProcess)
+
+    def test_initial_q_values(self):
+        np.testing.assert_array_equal(self.rw.q_values, [0.5, 0.5])
+
+    def test_q_values_property_returns_copy(self):
+        q = self.rw.q_values
+        q[0] = 999.0
+        np.testing.assert_array_equal(self.rw.q_values, [0.5, 0.5])
+
+    def test_reset_clears_state(self):
+        params = {"rl_alpha": 0.6, "rl_alpha_neg": 0.1, "scaler": 1.0}
+        self.rw.update(action=0, reward=1.0, trial_params=params)
+        assert not np.array_equal(self.rw.q_values, [0.5, 0.5])
+        self.rw.reset()
+        np.testing.assert_array_equal(self.rw.q_values, [0.5, 0.5])
+
+    def test_free_params(self):
+        assert self.rw.free_params == ["rl_alpha", "rl_alpha_neg", "scaler"]
+
+    def test_param_bounds(self):
+        bounds = self.rw.param_bounds
+        assert bounds["rl_alpha"] == (0.0, 1.0)
+        assert bounds["rl_alpha_neg"] == (0.0, 1.0)
+        assert bounds["scaler"] == (0.001, 10.0)
+
+    def test_default_params(self):
+        assert self.rw.default_params == {
+            "rl_alpha": 0.2,
+            "rl_alpha_neg": 0.2,
+            "scaler": 2.0,
+        }
+
+    def test_positive_prediction_error_uses_rl_alpha(self):
+        params = {"rl_alpha": 0.6, "rl_alpha_neg": 0.1, "scaler": 1.0}
+        self.rw.update(action=0, reward=1.0, trial_params=params)
+        np.testing.assert_allclose(self.rw.q_values, [0.8, 0.5])
+
+    def test_negative_prediction_error_uses_rl_alpha_neg(self):
+        params = {"rl_alpha": 0.6, "rl_alpha_neg": 0.1, "scaler": 1.0}
+        self.rw.update(action=0, reward=0.0, trial_params=params)
+        np.testing.assert_allclose(self.rw.q_values, [0.45, 0.5])
+
+    def test_drift_before_update_ordering(self):
+        params = {"rl_alpha": 0.6, "rl_alpha_neg": 0.1, "scaler": 2.0}
+        v_before = self.rw.compute_ssm_params(params)["v"]
+        assert v_before == 0.0
+        self.rw.update(action=0, reward=1.0, trial_params=params)
+        v_after = self.rw.compute_ssm_params(params)["v"]
+        assert v_after == pytest.approx(-0.6)
+
+    def test_multiple_updates_trajectory(self):
+        params = {"rl_alpha": 0.6, "rl_alpha_neg": 0.1, "scaler": 2.0}
+        sequence = [(0, 1.0), (0, 0.0), (1, 1.0), (1, 0.0)]
+        expected_v_before = [0.0, -0.6, -0.44, 0.16]
+        expected_q = [
+            [0.8, 0.5],
+            [0.72, 0.5],
+            [0.72, 0.8],
+            [0.72, 0.72],
+        ]
+
+        for i, (action, reward) in enumerate(sequence):
+            v = self.rw.compute_ssm_params(params)["v"]
+            assert v == pytest.approx(expected_v_before[i], abs=1e-12)
+            self.rw.update(action=action, reward=reward, trial_params=params)
+            np.testing.assert_allclose(self.rw.q_values, expected_q[i], atol=1e-12)
diff --git a/tests/rl/test_rl_config.py b/tests/rl/test_rl_config.py
new file mode 100644
index 00000000..936ec9e6
--- /dev/null
+++ b/tests/rl/test_rl_config.py
@@ -0,0 +1,305 @@
+"""Tests for rl.ModelConfig."""
+
+import pytest
+
+import ssms.rl as rl
+from ssms.rl.config import _HSSM_SHARED_FIELDS
+
+
+def _make_default_config(**overrides):
+    """Helper to create a default angle + RW config with optional overrides."""
+    defaults = dict(
+        model_name="test_rlssm",
+        description="Test RLSSM config",
+        decision_process="angle",
+        learning_process=rl.learning.RescorlaWagnerDeltaRule(
+            n_actions=2, initial_q=0.5
+        ),
+        task_environment=rl.env.Bandit.bernoulli(
+            probabilities=[0.7, 0.3], response_labels=[-1, 1]
+        ),
+    )
+    defaults.update(overrides)
+    return rl.ModelConfig(**defaults)
+
+
+class TestAutoDerivation:
+    def test_auto_derive_list_params(self):
+        config = _make_default_config()
+        # RL free params + fixed SSM params (v is computed, so excluded)
+        assert config.list_params == ["rl_alpha", "scaler", "a", "z", "t", "theta"]
+
+    def test_auto_derive_bounds(self):
+        config = _make_default_config()
+        bounds = config.bounds
+        # RL bounds
+        assert bounds["rl_alpha"] == (0.0, 1.0)
+        assert bounds["scaler"] == (0.001, 10.0)
+        # SSM bounds for fixed params
+        assert bounds["a"] == (0.3, 3.0)
+        assert bounds["z"] == (0.1, 0.9)
+        assert bounds["t"] == (0.001, 2.0)
+        assert bounds["theta"] == (-0.1, 1.3)
+        # v should NOT be in bounds (it's computed)
+        assert "v" not in bounds
+
+    def test_auto_derive_params_default(self):
+        config = _make_default_config()
+        # Order matches list_params
+        expected_names = ["rl_alpha", "scaler", "a", "z", "t", "theta"]
+        assert config.list_params == expected_names
+        assert len(config.params_default) == len(expected_names)
+        # RL defaults
+        assert config.params_default[0] == 0.2  # rl_alpha
+        assert config.params_default[1] == 2.0  # scaler
+        # SSM defaults for angle model
+        assert config.params_default[2] == 1.0  # a
+        assert config.params_default[3] == 0.5  # z
+
+    def test_auto_derive_choices(self):
+        config = _make_default_config()
+        assert config.choices == (-1, 1)
+
+    def test_auto_response_mapping(self):
+        config = _make_default_config(
+            task_environment=rl.env.Bandit.bernoulli(response_labels=[-1, 1])
+        )
+        assert config.response_to_action == {-1: 0, 1: 1}
+
+    def test_auto_derive_extra_fields(self):
+        config = _make_default_config()
+        assert config.extra_fields == ["feedback"]
+
+
+class TestDualAlphaAutoDerivation:
+    def test_auto_derive_list_params(self):
+        config = _make_default_config(
+            learning_process=rl.learning.RescorlaWagnerDualAlphaRule()
+        )
+        assert config.list_params == [
+            "rl_alpha",
+            "rl_alpha_neg",
+            "scaler",
+            "a",
+            "z",
+            "t",
+            "theta",
+        ]
+
+    def test_auto_derive_bounds(self):
+        config = _make_default_config(
+            learning_process=rl.learning.RescorlaWagnerDualAlphaRule()
+        )
+        assert config.bounds["rl_alpha"] == (0.0, 1.0)
+        assert config.bounds["rl_alpha_neg"] == (0.0, 1.0)
+        assert config.bounds["scaler"] == (0.001, 10.0)
+        assert "v" not in config.bounds
+
+    def test_auto_derive_params_default(self):
+        config = _make_default_config(
+            learning_process=rl.learning.RescorlaWagnerDualAlphaRule()
+        )
+        assert config.params_default[:3] == [0.2, 0.2, 2.0]
+
+
+class TestHandshakeValidation:
+    def test_valid_config_validates(self):
+        config = _make_default_config()
+        config.validate()  # Should not raise
+
+    def test_unknown_computed_param(self):
+        """Learning computes a param not in the SSM model."""
+
+        class BadLearning:
+            computed_params = ["q_diff"]  # Not an SSM param
+            free_params = ["alpha"]
+            param_bounds = {"alpha": (0.0, 1.0)}
+            default_params = {"alpha": 0.2}
+
+            def reset(self, **kwargs):
+                pass
+
+            def compute_ssm_params(self, trial_params):
+                return {"q_diff": 0.0}
+
+            def update(self, action, reward, trial_params):
+                pass
+
+        config = _make_default_config(learning_process=BadLearning())
+        with pytest.raises(ValueError, match="not params of the 'angle' SSM model"):
+            config.validate()
+
+    def test_computed_param_mapping(self):
+        """Learning computes 'drift' but SSM expects 'v' — mapping resolves it."""
+
+        class DriftLearning:
+            computed_params = ["drift"]
+            free_params = ["alpha"]
+            param_bounds = {"alpha": (0.0, 1.0)}
+            default_params = {"alpha": 0.2}
+
+            def reset(self, **kwargs):
+                pass
+
+            def compute_ssm_params(self, trial_params):
+                return {"drift": 0.0}
+
+            def update(self, action, reward, trial_params):
+                pass
+
+        config = _make_default_config(
+            learning_process=DriftLearning(),
+            computed_param_mapping={"drift": "v"},
+        )
+        config.validate()  # Should not raise
+        assert "v" not in config.bounds  # v is computed, not fixed
+
+    def test_computed_param_mapping_collision_raises(self):
+        """Multiple learning outputs cannot map to one SSM parameter."""
+
+        class CollidingLearning:
+            computed_params = ["drift_left", "drift_right"]
+            free_params = ["alpha"]
+            param_bounds = {"alpha": (0.0, 1.0)}
+            default_params = {"alpha": 0.2}
+
+            def reset(self, **kwargs):
+                pass
+
+            def compute_ssm_params(self, trial_params):
+                return {"drift_left": 0.0, "drift_right": 0.0}
+
+            def update(self, action, reward, trial_params):
+                pass
+
+        with pytest.raises(ValueError, match="computed_param_mapping"):
+            _make_default_config(
+                learning_process=CollidingLearning(),
+                computed_param_mapping={"drift_left": "v", "drift_right": "v"},
+            )
+
+
+class TestTaskConfigAutoBuild:
+    def test_task_config_auto_build(self):
+        config = _make_default_config(
+            task_environment=rl.env.TaskConfig(
+                task="bandit",
+                reward="bernoulli",
+                probabilities=[0.6, 0.4],
+                response_labels=[-1, 1],
+            ),
+        )
+        assert isinstance(config.task_environment, rl.env.Bandit)
+        assert config.choices == (-1, 1)
+
+    def test_gaussian_task_config_auto_build(self):
+        config = _make_default_config(
+            task_environment=rl.env.TaskConfig(
+                task="bandit",
+                reward="gaussian",
+                means=[1.0, 0.0],
+                sds=[0.25, 0.5],
+                response_labels=[-1, 1],
+            ),
+        )
+        assert isinstance(config.task_environment, rl.env.Bandit)
+        assert config.choices == (-1, 1)
+
+
+class TestResponseMapping:
+    def test_explicit_reversed_mapping(self):
+        config = _make_default_config(
+            task_environment=rl.env.Bandit.bernoulli(response_labels=[-1, 1]),
+            response_mapping={-1: 1, 1: 0},
+        )
+        assert config.response_to_action == {-1: 1, 1: 0}
+
+    def test_missing_mapping_label_raises(self):
+        with pytest.raises(ValueError, match="cover response labels exactly"):
+            _make_default_config(
+                task_environment=rl.env.Bandit.bernoulli(response_labels=[-1, 1]),
+                response_mapping={-1: 0},
+            )
+
+    def test_extra_mapping_label_raises(self):
+        with pytest.raises(ValueError, match="cover response labels exactly"):
+            _make_default_config(
+                task_environment=rl.env.Bandit.bernoulli(response_labels=[-1, 1]),
+                response_mapping={-1: 0, 1: 1, 0: 0},
+            )
+
+    def test_duplicate_mapping_values_raise(self):
+        with pytest.raises(ValueError, match="must be unique"):
+            _make_default_config(
+                task_environment=rl.env.Bandit.bernoulli(response_labels=[-1, 1]),
+                response_mapping={-1: 0, 1: 0},
+            )
+
+    def test_out_of_range_mapping_values_raise(self):
+        with pytest.raises(ValueError, match="must be exactly"):
+            _make_default_config(
+                task_environment=rl.env.Bandit.bernoulli(response_labels=[-1, 1]),
+                response_mapping={-1: 0, 1: 2},
+            )
+
+    def test_choices_must_match_response_labels(self):
+        with pytest.raises(ValueError, match="choices must match"):
+            _make_default_config(
+                task_environment=rl.env.Bandit.bernoulli(response_labels=[-1, 1]),
+                choices=(0, 1),
+            )
+
+    def test_response_labels_must_match_ssm_choices(self):
+        with pytest.raises(ValueError, match="SSM choices"):
+            _make_default_config(
+                task_environment=rl.env.Bandit.bernoulli(
+                    probabilities=[0.7, 0.3], response_labels=[0, 1]
+                ),
+                choices=(0, 1),
+            )
+
+    def test_include_action_defaults_false(self):
+        config = _make_default_config()
+        assert config.include_action is False
+
+
+class TestListParamsValidation:
+    def test_list_params_must_include_required_params(self):
+        config = _make_default_config(list_params=["rl_alpha", "scaler"])
+        with pytest.raises(ValueError, match="list_params must match"):
+            config.validate()
+
+
+class TestToHssmConfigDict:
+    def test_all_shared_fields_present(self):
+        config = _make_default_config()
+        d = config.to_hssm_config_dict()
+        for field_name in _HSSM_SHARED_FIELDS:
+            assert field_name in d, f"Missing field: {field_name}"
+            assert d[field_name] is not None, f"Field {field_name} is None"
+
+    def test_inference_placeholders(self):
+        config = _make_default_config()
+        d = config.to_hssm_config_dict()
+        assert d["ssm_logp_func"] is None
+        assert d["learning_process"] == {}
+        assert d["decision_process_loglik_kind"] == "approx_differentiable"
+        assert d["learning_process_kind"] == "blackbox"
+        assert "learning_process_loglik_kind" not in d
+
+    def test_contract_consistency(self):
+        """list_params length == params_default length, all have bounds."""
+        config = _make_default_config()
+        d = config.to_hssm_config_dict()
+        assert len(d["list_params"]) == len(d["params_default"])
+        for p in d["list_params"]:
+            assert p in d["bounds"], f"Missing bounds for {p}"
+
+    def test_values_match_config(self):
+        config = _make_default_config()
+        d = config.to_hssm_config_dict()
+        assert d["model_name"] == config.model_name
+        assert d["decision_process"] == config.decision_process
+        assert d["choices"] == config.choices
+        assert d["response"] == config.response
+        assert d["response_mapping"] == config.response_to_action
diff --git a/tests/rl/test_rl_simulator.py b/tests/rl/test_rl_simulator.py
new file mode 100644
index 00000000..95a496ed
--- /dev/null
+++ b/tests/rl/test_rl_simulator.py
@@ -0,0 +1,340 @@
+"""Tests for rl.Simulator."""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from ssms import OMISSION_SENTINEL
+import ssms.rl as rl
+from ssms.rl.simulator import MISSING_RESPONSE_SENTINEL
+
+
+def _make_simulator(**config_overrides):
+    """Create a simulator with sensible defaults for testing."""
+    defaults = dict(
+        model_name="test_rlssm",
+        description="Test RLSSM",
+        decision_process="angle",
+        learning_process=rl.learning.RescorlaWagnerDeltaRule(
+            n_actions=2, initial_q=0.5
+        ),
+        # Use response labels [-1, 1] to match angle model's SSM output
+        task_environment=rl.env.Bandit.bernoulli(
+            probabilities=[0.7, 0.3], response_labels=[-1, 1]
+        ),
+    )
+    defaults.update(config_overrides)
+    config = rl.ModelConfig(**defaults)
+    return rl.Simulator(config)
+
+
+# Default theta for angle model (v is computed by learning process)
+THETA = {"rl_alpha": 0.2, "scaler": 2.0, "a": 1.5, "z": 0.5, "t": 0.3, "theta": 0.2}
+THETA_DUAL = {
+    "rl_alpha": 0.2,
+    "rl_alpha_neg": 0.1,
+    "scaler": 2.0,
+    "a": 1.5,
+    "z": 0.5,
+    "t": 0.3,
+    "theta": 0.2,
+}
+
+
+class TestSimulateOutput:
+    @pytest.fixture()
+    def sim(self):
+        return _make_simulator()
+
+    @pytest.fixture()
+    def data(self, sim):
+        return sim.simulate(theta=THETA, n_trials=10, n_participants=3, random_state=42)
+
+    def test_returns_dataframe(self, data):
+        assert isinstance(data, pd.DataFrame)
+
+    def test_balanced_panel(self, data):
+        assert len(data) == 3 * 10
+        for pid in range(3):
+            assert len(data[data["participant_id"] == pid]) == 10
+
+    def test_columns(self, data):
+        expected = {"participant_id", "trial_id", "rt", "response", "feedback"}
+        assert expected.issubset(set(data.columns))
+
+    def test_default_excludes_action(self, data):
+        assert "action" not in data.columns
+
+    def test_sorted_order(self, data):
+        assert data.equals(
+            data.sort_values(["participant_id", "trial_id"]).reset_index(drop=True)
+        )
+
+    def test_participant_ids(self, data):
+        assert sorted(data["participant_id"].unique()) == [0, 1, 2]
+
+    def test_trial_ids(self, data):
+        for pid in range(3):
+            trials = sorted(data[data["participant_id"] == pid]["trial_id"].tolist())
+            assert trials == list(range(10))
+
+    def test_rt_positive(self, data):
+        non_omission = data[data["rt"] != OMISSION_SENTINEL]
+        assert (non_omission["rt"] > 0).all()
+
+    def test_response_in_labels(self, data):
+        non_omission = data[data["response"] != MISSING_RESPONSE_SENTINEL]
+        assert set(non_omission["response"].unique()).issubset({-1, 1})
+
+    def test_feedback_binary(self, data):
+        non_omission = data[data["rt"] != OMISSION_SENTINEL]
+        assert set(non_omission["feedback"].unique()).issubset({0.0, 1.0})
+
+
+class TestReproducibility:
+    def test_same_seed_same_result(self):
+        sim = _make_simulator()
+        df1 = sim.simulate(theta=THETA, n_trials=10, n_participants=2, random_state=42)
+        df2 = sim.simulate(theta=THETA, n_trials=10, n_participants=2, random_state=42)
+        pd.testing.assert_frame_equal(df1, df2)
+
+    def test_different_seed_different_result(self):
+        sim = _make_simulator()
+        df1 = sim.simulate(theta=THETA, n_trials=10, n_participants=2, random_state=1)
+        df2 = sim.simulate(theta=THETA, n_trials=10, n_participants=2, random_state=2)
+        assert not df1["rt"].equals(df2["rt"])
+
+
+class TestValidation:
+    def test_missing_theta_param(self):
+        sim = _make_simulator()
+        incomplete = {"rl_alpha": 0.2, "scaler": 2.0}  # missing SSM params
+        with pytest.raises(ValueError, match="theta is missing"):
+            sim.simulate(theta=incomplete, n_trials=5, n_participants=1)
+
+
+class TestEdgeCases:
+    def test_single_participant(self):
+        sim = _make_simulator()
+        df = sim.simulate(theta=THETA, n_trials=5, n_participants=1, random_state=42)
+        assert len(df) == 5
+        assert df["participant_id"].unique().tolist() == [0]
+
+    def test_single_trial(self):
+        sim = _make_simulator()
+        df = sim.simulate(theta=THETA, n_trials=1, n_participants=2, random_state=42)
+        assert len(df) == 2
+
+
+class TestOmissionHandling:
+    def test_omission_code_path(self):
+        """Verify that the omission sentinel is handled correctly by patching
+        the SSM simulator to return an omission on certain trials."""
+        from unittest.mock import patch
+
+        sim = _make_simulator()
+        call_count = 0
+
+        def mock_simulator(**kwargs):
+            nonlocal call_count
+            call_count += 1
+            # Return omission on trials 2 and 4 (0-indexed within each subject)
+            if call_count in (3, 5):
+                return {
+                    "rts": np.array([[OMISSION_SENTINEL]]),
+                    "choices": np.array([[-1]]),
+                }
+            return {
+                "rts": np.array([[0.5]]),
+                "choices": np.array([[1]]),
+            }
+
+        with patch("ssms.rl.simulator.ssm_simulator", side_effect=mock_simulator):
+            df = sim.simulate(
+                theta=THETA, n_trials=5, n_participants=1, random_state=42
+            )
+
+        assert len(df) == 5
+        omissions = df[df["rt"] == OMISSION_SENTINEL]
+        assert len(omissions) == 2
+        assert (omissions["response"] == MISSING_RESPONSE_SENTINEL).all()
+        assert (omissions["feedback"] == 0.0).all()
+        # Non-omission rows should have valid data
+        non_omission = df[df["rt"] != OMISSION_SENTINEL]
+        assert (non_omission["rt"] == 0.5).all()
+        assert set(non_omission["response"].unique()) == {1}
+
+    def test_omission_action_uses_missing_response_sentinel(self):
+        from unittest.mock import patch
+
+        sim = _make_simulator(include_action=True)
+
+        def mock_simulator(**kwargs):
+            return {
+                "rts": np.array([[OMISSION_SENTINEL]]),
+                "choices": np.array([[-1]]),
+            }
+
+        with patch("ssms.rl.simulator.ssm_simulator", side_effect=mock_simulator):
+            df = sim.simulate(
+                theta=THETA, n_trials=1, n_participants=1, random_state=42
+            )
+
+        assert df.loc[0, "response"] == MISSING_RESPONSE_SENTINEL
+        assert df.loc[0, "action"] == MISSING_RESPONSE_SENTINEL
+
+
+class TestResponseActionMapping:
+    def test_response_labels_are_mapped_to_learning_action_indices(self):
+        """Angle/DDM response labels [-1, 1] map to learning actions [0, 1]."""
+        from unittest.mock import patch
+
+        sim = _make_simulator(
+            task_environment=rl.env.Bandit.bernoulli(
+                probabilities=[1.0, 0.0], response_labels=[-1, 1]
+            )
+        )
+        choices = iter([-1, 1])
+        theta = {**THETA, "rl_alpha": 1.0}
+
+        def mock_simulator(**kwargs):
+            return {
+                "rts": np.array([[0.5]]),
+                "choices": np.array([[next(choices)]]),
+            }
+
+        with patch("ssms.rl.simulator.ssm_simulator", side_effect=mock_simulator):
+            df = sim.simulate(theta=theta, n_trials=2, n_participants=1)
+
+        assert df["response"].tolist() == [-1, 1]
+        np.testing.assert_allclose(
+            sim.config.learning_process.q_values, np.array([1.0, 0.0])
+        )
+
+    def test_unknown_response_label_raises(self):
+        from unittest.mock import patch
+
+        sim = _make_simulator()
+
+        def mock_simulator(**kwargs):
+            return {"rts": np.array([[0.5]]), "choices": np.array([[0]])}
+
+        with patch("ssms.rl.simulator.ssm_simulator", side_effect=mock_simulator):
+            with pytest.raises(ValueError, match="not in response_mapping"):
+                sim.simulate(theta=THETA, n_trials=1, n_participants=1)
+
+    def test_include_action_emits_derived_action(self):
+        from unittest.mock import patch
+
+        sim = _make_simulator(include_action=True)
+        choices = iter([-1, 1])
+
+        def mock_simulator(**kwargs):
+            return {
+                "rts": np.array([[0.5]]),
+                "choices": np.array([[next(choices)]]),
+            }
+
+        with patch("ssms.rl.simulator.ssm_simulator", side_effect=mock_simulator):
+            df = sim.simulate(theta=THETA, n_trials=2, n_participants=1)
+
+        assert df["response"].tolist() == [-1, 1]
+        assert df["action"].tolist() == [0, 1]
+
+    def test_reversed_mapping_changes_learning_updates(self):
+        from unittest.mock import patch
+
+        sim = _make_simulator(
+            task_environment=rl.env.Bandit.bernoulli(
+                probabilities=[1.0, 0.0], response_labels=[-1, 1]
+            ),
+            response_mapping={-1: 1, 1: 0},
+            include_action=True,
+        )
+        choices = iter([-1, 1])
+        theta = {**THETA, "rl_alpha": 1.0}
+
+        def mock_simulator(**kwargs):
+            return {
+                "rts": np.array([[0.5]]),
+                "choices": np.array([[next(choices)]]),
+            }
+
+        with patch("ssms.rl.simulator.ssm_simulator", side_effect=mock_simulator):
+            df = sim.simulate(theta=theta, n_trials=2, n_participants=1)
+
+        assert df["response"].tolist() == [-1, 1]
+        assert df["action"].tolist() == [1, 0]
+        np.testing.assert_allclose(
+            sim.config.learning_process.q_values, np.array([1.0, 0.0])
+        )
+
+
+class TestMilestone2Integration:
+    def test_dual_alpha_learning_rule_simulates(self):
+        sim = _make_simulator(
+            learning_process=rl.learning.RescorlaWagnerDualAlphaRule()
+        )
+
+        df = sim.simulate(
+            theta=THETA_DUAL,
+            n_trials=10,
+            n_participants=2,
+            random_state=42,
+        )
+
+        assert len(df) == 20
+        assert sim.config.list_params == [
+            "rl_alpha",
+            "rl_alpha_neg",
+            "scaler",
+            "a",
+            "z",
+            "t",
+            "theta",
+        ]
+
+    def test_gaussian_bandit_simulates_continuous_feedback(self):
+        sim = _make_simulator(
+            task_environment=rl.env.Bandit.gaussian(
+                means=[1.0, 0.0],
+                sds=[0.2, 0.2],
+                response_labels=[-1, 1],
+            )
+        )
+
+        df = sim.simulate(theta=THETA, n_trials=30, n_participants=2, random_state=42)
+        non_omission = df[df["rt"] != OMISSION_SENTINEL]
+
+        assert len(df) == 60
+        assert pd.api.types.is_float_dtype(df["feedback"])
+        assert not set(non_omission["feedback"].unique()).issubset({0.0, 1.0})
+
+    def test_gaussian_response_labels_map_to_learning_action_indices(self):
+        from unittest.mock import patch
+
+        sim = _make_simulator(
+            task_environment=rl.env.Bandit.gaussian(
+                means=[1.0, 0.0],
+                sds=[1e-12, 1e-12],
+                response_labels=[-1, 1],
+            )
+        )
+        choices = iter([-1, 1])
+        theta = {**THETA, "rl_alpha": 1.0}
+
+        def mock_simulator(**kwargs):
+            return {
+                "rts": np.array([[0.5]]),
+                "choices": np.array([[next(choices)]]),
+            }
+
+        with patch("ssms.rl.simulator.ssm_simulator", side_effect=mock_simulator):
+            df = sim.simulate(theta=theta, n_trials=2, n_participants=1)
+
+        assert df["response"].tolist() == [-1, 1]
+        np.testing.assert_allclose(
+            sim.config.learning_process.q_values,
+            np.array([1.0, 0.0]),
+            atol=1e-9,
+        )
diff --git a/tests/rl/test_task_environment.py b/tests/rl/test_task_environment.py
new file mode 100644
index 00000000..5cdf24a6
--- /dev/null
+++ b/tests/rl/test_task_environment.py
@@ -0,0 +1,245 @@
+"""Tests for TaskEnvironment protocol, Bandit, and TaskConfig."""
+
+import numpy as np
+import pytest
+
+from ssms.rl.env import Bandit, TaskConfig, TaskEnvironment, registered_tasks
+
+
+class TestBernoulliBandit:
+    def test_reward_statistics_two_arms(self):
+        bandit = Bandit.bernoulli(probabilities=[0.8, 0.2])
+        bandit.reset(rng=np.random.default_rng(42))
+
+        n = 10_000
+        rewards_0 = [bandit.sample_reward(0, t) for t in range(n)]
+        bandit.reset(rng=np.random.default_rng(43))
+        rewards_1 = [bandit.sample_reward(1, t) for t in range(n)]
+
+        assert np.mean(rewards_0) == pytest.approx(0.8, abs=0.02)
+        assert np.mean(rewards_1) == pytest.approx(0.2, abs=0.02)
+
+    def test_reward_statistics_three_arms(self):
+        bandit = Bandit.bernoulli(probabilities=[0.8, 0.5, 0.2])
+        n = 10_000
+
+        means = []
+        for action in range(3):
+            bandit.reset(rng=np.random.default_rng(42 + action))
+            rewards = [bandit.sample_reward(action, t) for t in range(n)]
+            means.append(np.mean(rewards))
+
+        assert means[0] == pytest.approx(0.8, abs=0.02)
+        assert means[1] == pytest.approx(0.5, abs=0.02)
+        assert means[2] == pytest.approx(0.2, abs=0.02)
+
+    def test_reproducibility(self):
+        bandit = Bandit.bernoulli()
+        bandit.reset(rng=np.random.default_rng(99))
+        seq1 = [bandit.sample_reward(0, t) for t in range(50)]
+        bandit.reset(rng=np.random.default_rng(99))
+        seq2 = [bandit.sample_reward(0, t) for t in range(50)]
+        assert seq1 == seq2
+
+    def test_different_seeds(self):
+        bandit = Bandit.bernoulli()
+        bandit.reset(rng=np.random.default_rng(1))
+        seq1 = [bandit.sample_reward(0, t) for t in range(50)]
+        bandit.reset(rng=np.random.default_rng(2))
+        seq2 = [bandit.sample_reward(0, t) for t in range(50)]
+        assert seq1 != seq2
+
+    def test_invalid_probability_out_of_range(self):
+        with pytest.raises(ValueError, match="not in \\[0, 1\\]"):
+            Bandit.bernoulli(probabilities=[1.5, 0.3])
+
+    def test_invalid_probability_too_few_arms(self):
+        with pytest.raises(ValueError, match="at least 2 arms"):
+            Bandit.bernoulli(probabilities=[0.7])
+
+    def test_empty_probabilities_are_invalid(self):
+        with pytest.raises(ValueError, match="at least 2 arms"):
+            Bandit.bernoulli(probabilities=[])
+
+    def test_response_labels_length_mismatch(self):
+        with pytest.raises(ValueError, match="response_labels length"):
+            Bandit.bernoulli(probabilities=[0.7, 0.3], response_labels=[0])
+
+    def test_duplicate_response_labels(self):
+        with pytest.raises(ValueError, match="response_labels must be unique"):
+            Bandit.bernoulli(probabilities=[0.7, 0.3], response_labels=[1, 1])
+
+    def test_reset_required(self):
+        bandit = Bandit.bernoulli()
+        with pytest.raises(RuntimeError, match="Call reset"):
+            bandit.sample_reward(0, 0)
+
+    def test_action_out_of_range(self):
+        bandit = Bandit.bernoulli()
+        bandit.reset()
+        with pytest.raises(ValueError, match="out of range"):
+            bandit.sample_reward(2, 0)
+
+    def test_protocol_compliance(self):
+        assert isinstance(Bandit.bernoulli(), TaskEnvironment)
+
+    def test_extra_fields_empty(self):
+        bandit = Bandit.bernoulli()
+        assert bandit.extra_fields == []
+        bandit.reset()
+        assert bandit.get_extra_data(0) == {}
+
+    def test_n_arms_and_response_labels(self):
+        bandit = Bandit.bernoulli(
+            probabilities=[0.4, 0.3, 0.2], response_labels=[-1, 0, 1]
+        )
+        assert bandit.n_arms == 3
+        assert bandit.response_labels == [-1, 0, 1]
+
+    def test_response_labels_returns_copy(self):
+        bandit = Bandit.bernoulli(response_labels=[0, 1])
+        labels = bandit.response_labels
+        labels.append(999)
+        assert bandit.response_labels == [0, 1]
+
+
+class TestGaussianRewards:
+    def test_reward_statistics_two_arms(self):
+        bandit = Bandit.gaussian(means=[1.5, -0.5], sds=[0.2, 0.8])
+        bandit.reset(rng=np.random.default_rng(42))
+
+        n = 20_000
+        rewards_0 = np.array([bandit.sample_reward(0, t) for t in range(n)])
+        bandit.reset(rng=np.random.default_rng(43))
+        rewards_1 = np.array([bandit.sample_reward(1, t) for t in range(n)])
+
+        assert rewards_0.mean() == pytest.approx(1.5, abs=0.01)
+        assert rewards_0.std() == pytest.approx(0.2, abs=0.01)
+        assert rewards_1.mean() == pytest.approx(-0.5, abs=0.02)
+        assert rewards_1.std() == pytest.approx(0.8, abs=0.02)
+
+    def test_reward_statistics_three_arms(self):
+        bandit = Bandit.gaussian(means=[1.5, 0.5, -0.5], sds=[0.2, 0.4, 0.8])
+        n = 20_000
+
+        observed = []
+        for action in range(3):
+            bandit.reset(rng=np.random.default_rng(42 + action))
+            rewards = np.array([bandit.sample_reward(action, t) for t in range(n)])
+            observed.append((rewards.mean(), rewards.std()))
+
+        assert observed[0][0] == pytest.approx(1.5, abs=0.01)
+        assert observed[0][1] == pytest.approx(0.2, abs=0.01)
+        assert observed[1][0] == pytest.approx(0.5, abs=0.01)
+        assert observed[1][1] == pytest.approx(0.4, abs=0.01)
+        assert observed[2][0] == pytest.approx(-0.5, abs=0.02)
+        assert observed[2][1] == pytest.approx(0.8, abs=0.02)
+
+    def test_reproducibility(self):
+        bandit = Bandit.gaussian()
+        bandit.reset(rng=np.random.default_rng(99))
+        seq1 = [bandit.sample_reward(0, t) for t in range(50)]
+        bandit.reset(rng=np.random.default_rng(99))
+        seq2 = [bandit.sample_reward(0, t) for t in range(50)]
+        assert seq1 == seq2
+
+    def test_invalid_means_too_few_arms(self):
+        with pytest.raises(ValueError, match="at least 2 arms"):
+            Bandit.gaussian(means=[1.0], sds=[1.0])
+
+    def test_empty_means_are_invalid(self):
+        with pytest.raises(ValueError, match="at least 2 arms"):
+            Bandit.gaussian(means=[], sds=[])
+
+    def test_invalid_sds_length_mismatch(self):
+        with pytest.raises(ValueError, match="means length"):
+            Bandit.gaussian(means=[1.0, 0.0], sds=[1.0])
+
+    def test_empty_sds_are_invalid(self):
+        with pytest.raises(ValueError, match="means length"):
+            Bandit.gaussian(means=[1.0, 0.0], sds=[])
+
+    def test_invalid_sd_non_positive(self):
+        with pytest.raises(ValueError, match="must be positive"):
+            Bandit.gaussian(sds=[1.0, 0.0])
+
+    def test_reset_required(self):
+        bandit = Bandit.gaussian()
+        with pytest.raises(RuntimeError, match="Call reset"):
+            bandit.sample_reward(0, 0)
+
+    def test_protocol_compliance(self):
+        assert isinstance(Bandit.gaussian(), TaskEnvironment)
+
+    def test_n_arms_and_response_labels(self):
+        bandit = Bandit.gaussian(
+            means=[1.0, 0.0, -1.0],
+            sds=[0.2, 0.4, 0.6],
+            response_labels=[-1, 0, 1],
+        )
+        assert bandit.n_arms == 3
+        assert bandit.response_labels == [-1, 0, 1]
+
+
+class TestTaskConfig:
+    def test_default_build(self):
+        env = TaskConfig().build_environment()
+        assert isinstance(env, Bandit)
+        assert env.n_arms == 2
+
+    def test_registered_tasks(self):
+        assert "bandit" in registered_tasks()
+
+    def test_builds_bernoulli(self):
+        env = TaskConfig(
+            task="bandit",
+            reward="bernoulli",
+            probabilities=[0.6, 0.4],
+        ).build_environment()
+        assert isinstance(env, Bandit)
+        assert env.n_arms == 2
+
+    def test_builds_gaussian(self):
+        env = TaskConfig(
+            task="bandit",
+            reward="gaussian",
+            means=[2.0, -1.0],
+            sds=[0.5, 1.5],
+        ).build_environment()
+        assert isinstance(env, Bandit)
+        assert env.n_arms == 2
+
+    def test_custom_response_labels(self):
+        env = TaskConfig(
+            task="bandit",
+            reward="bernoulli",
+            probabilities=[0.5, 0.3, 0.2],
+            response_labels=[10, 20, 30],
+        ).build_environment()
+        assert env.response_labels == [10, 20, 30]
+        assert env.n_arms == 3
+
+    def test_gaussian_custom_response_labels(self):
+        env = TaskConfig(
+            task="bandit",
+            reward="gaussian",
+            means=[2.0, 1.0, 0.0],
+            sds=[0.2, 0.4, 0.6],
+            response_labels=[10, 20, 30],
+        ).build_environment()
+        assert env.response_labels == [10, 20, 30]
+        assert env.n_arms == 3
+
+    def test_unknown_task(self):
+        with pytest.raises(ValueError, match="Unknown task"):
+            TaskConfig(task="maze").build_environment()
+
+    def test_unknown_bandit_reward(self):
+        with pytest.raises(ValueError, match="Unknown bandit reward"):
+            TaskConfig(task="bandit", reward="exponential").build_environment()
+
+    def test_unknown_option(self):
+        with pytest.raises(TypeError, match="Unsupported options"):
+            TaskConfig(
+                task="bandit", reward="bernoulli", means=[1.0, 0.0]
+            ).build_environment()