src/lunar-lander/train.py

from tqdm import trange

def fill_memory(agent, env, num_episodes=500 ):
    print("Filling up memory....")
    for _ in trange(500):
        state = env.reset()
        done = False 
        while not done:
            action = agent.act(state)
            next_state, reward, done, _ = env.step(action)
            agent.cache(state, next_state, action, reward, done)
            state = next_state  


# def train(agent, env, logger):
#     episodes = 5000
#     for e in range(episodes):

#         state = env.reset()
#         # Play the game!
#         while True:
        
#             # Run agent on the state
#             action = agent.act(state)
            
#             # Agent performs action
#             next_state, reward, done, info = env.step(action)
            
#             # Remember
#             agent.cache(state, next_state, action, reward, done)

#             # Learn
#             q, loss = agent.learn()

#             # Logging
#             logger.log_step(reward, loss, q)

#             # Update state
#             state = next_state
            
#             # Check if end of game
#             if done:
#                 break
        
#         logger.log_episode(e)

#         if e % 20 == 0:
#             logger.record(episode=e, epsilon=agent.exploration_rate, step=agent.curr_step)


def train(agent, env, logger):
    episodes = 5000
    for e in range(episodes):

        state = env.reset()
        # Play the game!
        for i in range(1000):
        
            # Run agent on the state
            action = agent.act(state)
            env.render()
            # Agent performs action
            next_state, reward, done, info = env.step(action)
            
            # Remember
            agent.cache(state, next_state, action, reward, done, i)

            # Learn
            q, loss = agent.learn()

            # Logging
            logger.log_step(reward, loss, q)

            # Update state
            state = next_state
            
            # Check if end of game
            if done:
                break
        
        logger.log_episode(e)

        if e % 20 == 0:
            logger.record(episode=e, epsilon=agent.exploration_rate, step=agent.curr_step)