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
# 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)