import algorithms as alg
env = gym.make("MountainCar-v0")
bd = alg.encoding.BoxDiscretization(
env.observation_space,
N_buckets=[10, 10],
limits=[[env.observation_space.low[0], env.observation_space.high[0]],
[env.observation_space.low[1], env.observation_space.high[1]]])
env = gym.wrappers.TransformObservation(env, bd.encode)
print("Q-Learning")
alg.utils.random_seed(env, 1)
Q, history_qlearning = alg.qlearning(env,
alpha=0.1,
gamma=1,
epsilon=0.1,
N_episodes=2000)
alg.utils.plot_learning_curves([history_qlearning], ["Q-Learning"],
"mountaincar_td_learning.pdf")
for i in range(10):
done = False
state = env.reset()
steps = 1
ret = 0
while not done:
action = alg.utils.select_action_greedy(Q[state])
state, reward, done, info = env.step(action)
env.render()
alpha=0.1,
gamma=1,
epsilon=1,
N_episodes=10000,
epsilon_decay=alg.utils.decay_sigmoid)
pi = alg.utils.create_greedy_policy(Q)
print(
np.array([np.argmax(pi[s])
for s in range(env.nS)]).reshape(env.nrow, env.ncol))
evaluate_policy(env, pi, 10000, env.nS - 1)
print("\nQ-Learning")
alg.utils.random_seed(env, 1)
Q, history_qlearning = alg.qlearning(env,
alpha=0.1,
gamma=1,
epsilon=1,
N_episodes=10000,
epsilon_decay=alg.utils.decay_sigmoid)
pi = alg.utils.create_greedy_policy(Q)
print(
np.array([np.argmax(pi[s])
for s in range(env.nS)]).reshape(env.nrow, env.ncol))
evaluate_policy(env, pi, 10000, env.nS - 1)
print("\nExpected SARSA")
alg.utils.random_seed(env, 1)
Q, history_expected_sarsa = alg.expected_sarsa(
env,
alpha=0.1,
gamma=1,
epsilon=1,
Q, history_sarsa = alg.sarsa(env,
alpha=0.5,
gamma=0.99,
epsilon=0.5,
N_episodes=10000,
epsilon_decay=alg.utils.decay_linear,
alpha_decay=alg.utils.decay_linear)
pi = alg.utils.create_greedy_policy(Q)
evaluate_policy(env, pi, 1000)
print("\nQ-Learning")
alg.utils.random_seed(env, 1)
Q, history_qlearning = alg.qlearning(env,
alpha=0.5,
gamma=0.99,
epsilon=0.5,
N_episodes=10000,
epsilon_decay=alg.utils.decay_linear,
alpha_decay=alg.utils.decay_linear)
pi = alg.utils.create_greedy_policy(Q)
evaluate_policy(env, pi, 1000)
print("\nExpected SARSA")
alg.utils.random_seed(env, 1)
Q, history_expected_sarsa = alg.expected_sarsa(
env,
alpha=0.5,
gamma=0.99,
epsilon=0.5,
N_episodes=10000,
epsilon_decay=alg.utils.decay_linear,