tf2_policy_gradient_training.py

# if you have more than 1 gpu, use device '0' or '1' to assign to a gpu
#import os
#os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import gym
import numpy as np
from reinforce_tf2 import Agent
# from utils import plotLearning

if __name__ == '__main__':
    agent = Agent(alpha=0.0005,  gamma=0.99,n_actions=4)

    env = gym.make('LunarLander-v2')
    score_history = []

    num_episodes = 2000

    # TOPIC: (DRL) Training a DRL Network Based on the Monte-Carlo Method
    # To get a better understanding, look at the schematics demonstrated in LINK-14
    # 
    for i in range(num_episodes):
        done = False
        score = 0
        observation = env.reset()
        while not done:
            action = agent.choose_action(observation)
            observation_, reward, done, info = env.step(action)
            agent.store_transition(observation, action, reward)
            observation = observation_
            score += reward
        score_history.append(score)

        agent.learn()
        avg_score = np.mean(score_history[-100:])
        print('episode: ', i,'score: %.1f' % score,
            'average score %.1f' % avg_score)

    filename = 'lunar-lander.png'
    # plotLearning(score_history, filename=filename, window=100)