#!/usr/bin/env python3
"""This is an example to train a task with TRPO algorithm.

Here it runs CartPole-v1 environment with 100 iterations.

Results:
    AverageReturn: 100
    RiseTime: itr 13
"""
from garage import wrap_experiment
from garage.envs import GarageEnv
from garage.experiment import LocalTFRunner
from garage.experiment.deterministic import set_seed
from garage.np.baselines import LinearFeatureBaseline
from garage.tf.algos import TRPO
from garage.tf.policies import CategoricalMLPPolicy


@wrap_experiment
def trpo_cartpole(ctxt=None, seed=1):
    """Train TRPO with CartPole-v1 environment.

    Args:
        ctxt (garage.experiment.ExperimentContext): The experiment
            configuration used by LocalRunner to create the snapshotter.
        seed (int): Used to seed the random number generator to produce
            determinism.

    """
    set_seed(seed)
    with LocalTFRunner(ctxt) as runner:
        env = GarageEnv(env_name='CartPole-v1')

        policy = CategoricalMLPPolicy(name='policy',
                                      env_spec=env.spec,
                                      hidden_sizes=(32, 32))

        baseline = LinearFeatureBaseline(env_spec=env.spec)

        algo = TRPO(env_spec=env.spec,
                    policy=policy,
                    baseline=baseline,
                    max_path_length=100,
                    discount=0.99,
                    max_kl_step=0.01)

        runner.setup(algo, env)
        runner.train(n_epochs=100, batch_size=4000)


trpo_cartpole()
