def main():
# step 1: loading the environment
env = gym.make("FrozenLake-v0")
# step 2: creating the Q-table
state_size = env.observation_space.n
action_size = env.action_space.n
q = QTable(state_size, action_size)
# step 3: creating de epsilon decay
e = Epsilon(initial_epsilon=1.0, max_epsilon=1.0, min_epsilon=0.01, decay_rate=0.005)
# step 4: Q-table training
total_episodes = 100000
max_steps = 100
q, rewards = train_qtable(env, q, e, total_episodes, max_steps)
print("Score over time {:.4f}".format(sum(rewards) / total_episodes))
q.print()
# Play
env.reset()
rewards = []
for episode in range(1000):
state = env.reset()
step = 0
total_rewards = 0
for step in range(100):
action = q.select_action(env, state)
new_state, reward, done, info = env.step(action)
total_rewards += reward
state = new_state
if done:
break
rewards.append(total_rewards)
if episode % 100 == 0:
print("******************************************")
print("EPISODE {}".format(episode))
print("Number of steps: {}".format(step))
env.render()
print("Score over time {:.4f}".format(sum(rewards) / 1000))
env.close()
def main():
# Step 1: create the Taxi-v2 environment
env = gym.make("Taxi-v2")
# Step 2: create the QTable
q = QTable(env.observation_space.n, env.action_space.n, learning_rate=0.7, gamma=0.99)
# Step 3: create the Epsilon decay
e = Epsilon()
# Step 4: Q-table training
total_episodes = 100000
max_steps = 100
q, rewards = train_qtable(env, q, e, total_episodes, max_steps, verbose=True)
print("Score over time {:.4f}".format(sum(rewards) / total_episodes))
q.print()
env.render()
# Play
env.reset()
rewards = []
for episode in range(1000):
state = env.reset()
step = 0
total_rewards = 0
for step in range(100):
action = q.select_action(env, state)
new_state, reward, done, info = env.step(action)
total_rewards += reward
state = new_state
if done:
break
rewards.append(total_rewards)
if episode % 100 == 0:
print("******************************************")
print("EPISODE {}".format(episode))
print("Number of steps: {}".format(step))
env.render()
print("Score over time {:.4f}".format(sum(rewards) / 1000))
env.close()