コード例 #1
0
    def __init__(self, env_name, env, num_episodes, n_step=1, discount_factor=0.95, learning_rate=0.01, start_learning_rate=0.1, start_epsilon=1.0,
                 decay_rate=0.001, action_space_n=None, render_env=False, make_checkpoint=False, is_state_box=False, batch_size=25,
                 memory_capacity=1000):

        self.start_time = 0
        self.env_name = env_name
        self.env = env
        self.MAX_STEPS = 200
        self.num_episodes = num_episodes
        self.start_learning_rate = start_learning_rate
        self.learning_rate = learning_rate
        self.discount_factor = discount_factor
        self.start_epsilon = start_epsilon
        self.epsilon = 0
        self.decay_rate = decay_rate
        self.make_checkpoint = make_checkpoint
        self.n_step = n_step
        self.dir_location = "/home/dsalwala/NUIG/Thesis/rl-algos/data"
        self.is_state_box = is_state_box
        self.action_space_n = action_space_n
        self.render_env = render_env
        self.batch_size = batch_size
        self.memory_capacity = memory_capacity
        self.stats = plotting.EpisodeStats(
            episode_lengths=np.zeros(num_episodes),
            episode_rewards=np.zeros(num_episodes))

        if action_space_n is None:
            self.nA = env.action_space.n
        else:
            self.nA = action_space_n

        if self.is_state_box:
            self.nS = self.env.observation_space.shape[0]
        else:
            self.nS = 1
コード例 #2
0
        # Select best action to perform in a current state
        action = np.argmax(Q[state])

        # Perform an action an observe how environment acted in response
        next_state, reward, terminated, info = env.step(action)

        # Update current state
        state = next_state

        # Calculate number of wins over episodes
        if terminated and reward == 1.0:
            break


# Load a Windy GridWorld environment
environment = CliffWalkingEnv()
agent = QLearningAgent("CliffWalking-v0", environment, 1000, start_learning_rate=0.1, start_epsilon=1.0,
                       discount_factor=0.95, decay_rate=0.001, make_checkpoint=True)
# agent.train()
Q, rewards, episode_len = agent.load("/home/dsalwala/NUIG/Thesis/rl-algos/data/CliffWalking-v0_1000.npy")
stats = plotting.EpisodeStats(
    episode_lengths=episode_len,
    episode_rewards=rewards)

# Search for a Q values
# Q, stats = agent.q_table, agent.stats

play_episode(environment, Q)

plotting.plot_episode_stats(stats)