trpo_cartpole.py

from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.normalized_env import normalize
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
from rllab.misc.instrument import stub, run_experiment_lite

def run_task(*_):
	env = normalize(CartpoleEnv())

	policy = GaussianMLPPolicy(
	    env_spec=env.spec,
	    # The neural network policy should have two hidden layers, each with 32 hidden units.
	    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=0.01,
	    plot=True,
	)
	algo.train()

run_experiment_lite(
	run_task,
	n_parallel=4,
	snapshot_mode="last",
	seed=1,
	plot=True,
)