mainLoop.py

import gym
from model import DeepQNetwork, Agent
from utils import plotLearning
import numpy as np
from gym import wrappers

if __name__ == '__main__':
    env = gym.make('SpaceInvaders-v0')
    brain = Agent(gamma=0.95, epsilon=1.0,
                  alpha=0.003, maxMemorySize=5000,
                  replace=None)
    while brain.memCntr < brain.memSize:
        observation = env.reset()
        done = False
        while not done:
            # 0 no action, 1 fire, 2 move right, 3 move left, 4 move right fire, 5 move left fire
            action = env.action_space.sample()
            observation_, reward, done, info = env.step(action)
            if done and info['ale.lives'] == 0:
                reward = -100
            brain.storeTransition(np.mean(observation[15:200,30:125], axis=2), action, reward,
                                np.mean(observation_[15:200,30:125], axis=2))
            observation = observation_
    print('done initializing memory')

    scores = []
    epsHistory = []
    numGames = 50
    batch_size=32
    # uncomment the line below to record every episode.
    env = wrappers.Monitor(env, "tmp/space-invaders-1", video_callable=lambda episode_id: True, force=True)
    for i in range(numGames):
        print('starting game ', i+1, 'epsilon: %.4f' % brain.EPSILON)
        epsHistory.append(brain.EPSILON)
        done = False
        observation = env.reset()
        frames = [np.sum(observation[15:200,30:125], axis=2)]
        score = 0
        lastAction = 0
        while not done:
            if len(frames) == 3:
                action = brain.chooseAction(frames)
                frames = []
            else:
                action = lastAction
            observation_, reward, done, info = env.step(action)
            score += reward
            frames.append(np.sum(observation_[15:200,30:125], axis=2))
            if done and info['ale.lives'] == 0:
                reward = -100
            brain.storeTransition(np.mean(observation[15:200,30:125], axis=2), action, reward,
                                  np.mean(observation_[15:200,30:125], axis=2))
            observation = observation_
            brain.learn(batch_size)
            lastAction = action
            #env.render(
        scores.append(score)
        print('score:',score)
    x = [i+1 for i in range(numGames)]
    fileName = str(numGames) + 'Games' + 'Gamma' + str(brain.GAMMA) + \
               'Alpha' + str(brain.ALPHA) + 'Memory' + str(brain.memSize)+ '.png'
    plotLearning(x, scores, epsHistory, fileName)