def q_learning_greedy_probability_policy():
env = gym.make('FrozenLake-v0')
q_learning = QLearning(env.action_space.n,
env.observation_space.n,
epsilon=0.1,
learning_rate=0.1)
q_learning.set_general_state_action_values([0.5, 1, 0.5, 0.5])
episode_rewards = []
all_over_reward = 0.0
for i_episode in range(7000):
# We start a new episode with have to reset the environment and stats
observation = env.reset()
accumulated_reward = 0.0
for t in range(100):
# Show current state
# env.render()
# Choose action based on current experience
action = q_learning.greedy_probability_policy(observation)
# Save previous state, and commit action, resulting new current state
previous_observation = observation
observation, reward, done, info = env.step(action)
# Accumulate more reward
accumulated_reward += reward
# Train algorithm based on new experience
q_learning.update_state_action_function(previous_observation,
action, reward,
observation)
#
if done:
print "Episode finished after {} timesteps".format(t + 1)
print "Total reward for episode %i: %i" % (i_episode,
accumulated_reward)
all_over_reward += accumulated_reward
episode_rewards.append(accumulated_reward)
break
plot = Plot()
plot.plot_evolution(episode_rewards)
print q_learning.q_table
q_learning.write_q_function_dump()
def greedy_probability_policy():
env = gym.make('FrozenLake-v0')
q_learning = QLearning(env.action_space.n, env.observation_space.n, 0.9)
q_learning.set_general_state_action_values([0.5, 1, 0.5, 0.5])
for i_episode in range(20):
observation = env.reset()
for t in range(100):
env.render()
print(observation)
action = q_learning.greedy_probability_policy(observation)
observation, reward, done, info = env.step(action)
if done:
print "Episode finished after {} timesteps".format(t + 1)
break