def test_cem_cartpole(self):
"""Test CEM with Cartpole-v1 environment."""
with LocalTFRunner(snapshot_config) as runner:
env = GymEnv('CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
n_samples = 10
algo = CEM(env_spec=env.spec,
policy=policy,
baseline=baseline,
best_frac=0.1,
n_samples=n_samples)
runner.setup(algo, env, sampler_cls=LocalSampler)
rtn = runner.train(n_epochs=10, batch_size=2048)
assert rtn > 40
env.close()
def run_task(snapshot_config, *_):
"""Train CEM"""
with LocalTFRunner(snapshot_config=snapshot_config) as runner:
env = TfEnv(env_name='Swimmer-v2')
policy = GaussianMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
n_samples = 20
algo = CEM(env_spec=env.spec,
policy=policy,
baseline=baseline,
best_frac=0.05,
max_path_length=100,
n_samples=n_samples)
runner.setup(algo, env, sampler_cls=OnPolicyVectorizedSampler)
# NOTE: make sure that n_epoch_cycles == n_samples !
runner.train(n_epochs=100, batch_size=1000, n_epoch_cycles=n_samples)