def run_task(v):
env = normalize(CartpoleEnv())
policy = GaussianMLPPolicy(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=100,
n_itr=40,
discount=0.99,
step_size=v["step_size"],
# plot=True,
)
algo.train()
def run_task(*_):
env = TheanoEnv(normalize(CartpoleEnv()))
policy = GaussianMLPPolicy(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=100,
n_itr=1000,
discount=0.99,
step_size=0.01,
# Uncomment both lines (this and the plot parameter below) to enable
# plotting
#plot=True
)
algo.train()