fix: replace deprecated np.float and np.int aliases for NumPy 1.24+

8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb

@@ -246,7 +246,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+    "Q = np.ones((width,height,len(actions)),dtype=np.float64)*1.0/len(actions)"
    ]
   },
   {

8-Reinforcement/1-QLearning/solution/notebook.ipynb

@@ -264,7 +264,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+    "Q = np.ones((width,height,len(actions)),dtype=np.float64)*1.0/len(actions)"
    ]
   },
   {

8-Reinforcement/2-Gym/README.md

@@ -24,9 +24,9 @@ In this lesson, we will be using a library called **OpenAI Gym** to simulate dif
 
 ## OpenAI Gym
 
-In the previous lesson, the rules of the game and the state were given by the `Board` class which we defined ourselves. Here we will use a special **simulation environment**, which will simulate the physics behind the balancing pole. One of the most popular simulation environments for training reinforcement learning algorithms is called a [Gym](https://gym.openai.com/), which is maintained by [OpenAI](https://openai.com/). By using this gym we can create difference **environments** from a cartpole simulation to Atari games.
+In the previous lesson, the rules of the game and the state were given by the `Board` class which we defined ourselves. Here we will use a special **simulation environment**, which will simulate the physics behind the balancing pole. One of the most popular simulation environments for training reinforcement learning algorithms is called a [Gym](https://gymnasium.farama.org/), which is maintained by [OpenAI](https://openai.com/). By using this gym we can create difference **environments** from a cartpole simulation to Atari games.
 
-> **Note**: You can see other environments available from OpenAI Gym [here](https://gym.openai.com/envs/#classic_control). 
+> **Note**: You can see other environments available from OpenAI Gym [here](https://gymnasium.farama.org/environments/classic_control/). 
 
 First, let's install the gym and import required libraries (code block 1):
 

8-Reinforcement/2-Gym/assignment.md

@@ -1,10 +1,10 @@
 # Train Mountain Car
 
-[OpenAI Gym](http://gym.openai.com) has been designed in such a way that all environments provide the same API - i.e. the same methods `reset`, `step` and `render`, and the same abstractions of **action space** and **observation space**. Thus is should be possible to adapt the same reinforcement learning algorithms to different environments with minimal code changes.
+[OpenAI Gym](https://gymnasium.farama.org) has been designed in such a way that all environments provide the same API - i.e. the same methods `reset`, `step` and `render`, and the same abstractions of **action space** and **observation space**. Thus is should be possible to adapt the same reinforcement learning algorithms to different environments with minimal code changes.
 
 ## A Mountain Car Environment
 
-[Mountain Car environment](https://gym.openai.com/envs/MountainCar-v0/) contains a car stuck in a valley:
+[Mountain Car environment](https://gymnasium.farama.org/environments/classic_control/mountain_car/) contains a car stuck in a valley:
 
 <img src="images/mountaincar.png" width="300"/>
 

8-Reinforcement/2-Gym/solution/notebook.ipynb

@@ -209,7 +209,7 @@
    "outputs": [],
    "source": [
     "def discretize(x):\n",
-    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))"
+    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int_))"
    ]
   },
   {