def run_task(*_):
# Please note that different environments with different action spaces may
# require different policies. For example with a Discrete action space, a
# CategoricalMLPPolicy works, but for a Box action space may need to use
# a GaussianMLPPolicy (see the trpo_gym_pendulum.py example)
env = TheanoEnv(normalize(gym.make("CartPole-v0")))
policy = CategoricalMLPPolicy(env_spec=env.spec, hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=env.max_episode_steps,
n_itr=50,
discount=0.99,
step_size=0.01,
# Uncomment both lines (this and the plot parameter below) to enable
# plotting
plot=True,
)
algo.train()
def run_task(*_):
env = TheanoEnv(normalize(gym.make("Acrobot-v1")))
policy = CategoricalMLPPolicy(env_spec=env.spec, hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=env.max_episode_steps,
n_itr=50,
discount=0.99,
step_size=0.01,
plot=True,
)
algo.train()