def pendulum_with_qlearning():
from gym.wrappers import Monitor
env = gym.make('Pendulum-v0')
experiment_filename = './pendulum-experiment-1'
#env = Monitor(env, experiment_filename, force=True)
observation = env.reset()
goal_average_steps = -140
max_number_of_steps = 200
number_of_iterations_to_average = 100
last_time_steps = np.ndarray(0)
learner = QLearner(state_discretization=Binning([[-1., 1.], [-1., 1.], [-8., 8]], [10, 10, 40]),
discrete_actions=np.linspace(-2., 2., 10).tolist(),
alpha=0.2,
gamma=1,
random_action_rate=0.9,
random_action_decay_rate=0.99,
action_type="numpy")
cum_reward = 0.
for episode in range(5000):
cum_reward = 0.
env.reset()
action = learner.set_initial_state(observation)
done = False
for step in range(max_number_of_steps - 1):
observation, reward, done, info = env.step(action)
cum_reward += reward
if not done or step < max_number_of_steps -2:
action = learner.move(observation, reward)
if done:
print(cum_reward)
last_time_steps = np.append(last_time_steps, [cum_reward])
if len(last_time_steps) > number_of_iterations_to_average:
last_time_steps = np.delete(last_time_steps, 0)
observation = env.reset()
cum_reward = 0.
break
# if last_time_steps.mean() > goal_average_steps:
# print "Goal reached!"
# print "Episodes before solve: ", episode + 1
# print u"Best 100-episode performance {} {} {}".format(last_time_steps.max(),
# unichr(177), # plus minus sign
# last_time_steps.std())
# break
if not done:
print(cum_reward)
env.close()
def cart_pole_with_qlearning():
from gym.wrappers import Monitor
env = gym.make('CartPole-v0')
experiment_filename = './cartpole-experiment-1'
env = Monitor(env, experiment_filename, force=True)
observation = env.reset()
goal_average_steps = 195
max_number_of_steps = 200
number_of_iterations_to_average = 100
number_of_features = env.observation_space.shape[0]
last_time_steps = np.ndarray(0)
cart_position_bins = pd.cut([-2.4, 2.4], bins=10, retbins=True)[1][1:-1]
pole_angle_bins = pd.cut([-2, 2], bins=10, retbins=True)[1][1:-1]
cart_velocity_bins = pd.cut([-1, 1], bins=10, retbins=True)[1][1:-1]
angle_rate_bins = pd.cut([-3.5, 3.5], bins=10, retbins=True)[1][1:-1]
learner = QLearner(state_discretization=Binning([[-2.4, 2.4], [-2, 2], [-1., 1], [-3.5, 3.5]], [10] * 4),
discrete_actions=[i for i in range(env.action_space.n)],
alpha=0.2,
gamma=1,
random_action_rate=0.5,
random_action_decay_rate=0.99)
for episode in range(50000):
action = learner.set_initial_state(observation)
for step in range(max_number_of_steps - 1):
observation, reward, done, info = env.step(action)
if done:
reward = -200
observation = env.reset()
action = learner.move(observation, reward)
if done:
last_time_steps = np.append(last_time_steps, [int(step + 1)])
if len(last_time_steps) > number_of_iterations_to_average:
last_time_steps = np.delete(last_time_steps, 0)
break
if last_time_steps.mean() > goal_average_steps:
print "Goal reached!"
print "Episodes before solve: ", episode + 1
print u"Best 100-episode performance {} {} {}".format(last_time_steps.max(),
unichr(177), # plus minus sign
last_time_steps.std())
break
env.close()