def reps_gym_cartpole(ctxt=None, seed=1):
"""Train REPS with CartPole-v0 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(snapshot_config=ctxt) as runner:
env = GarageEnv(gym.make('CartPole-v0'))
policy = CategoricalMLPPolicy(env_spec=env.spec, hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = REPS(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99)
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=4000, plot=False)
def reps_gym_cartpole(ctxt=None, seed=1):
"""Train REPS with CartPole-v0 environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
"""
set_seed(seed)
with TFTrainer(snapshot_config=ctxt) as trainer:
env = GymEnv('CartPole-v0')
policy = CategoricalMLPPolicy(env_spec=env.spec, hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
sampler = RaySampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
is_tf_worker=True)
algo = REPS(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99)
trainer.setup(algo, env)
trainer.train(n_epochs=100, batch_size=4000, plot=False)
def test_reps_cartpole(self):
"""Test REPS with gym Cartpole environment."""
with LocalRunner(self.sess) as runner:
env = TfEnv(gym.make('CartPole-v0'))
policy = CategoricalMLPPolicy(env_spec=env.spec,
hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = REPS(env_spec=env.spec,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=10,
discount=0.99,
max_kl_step=1e6)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=4000)
assert last_avg_ret > 5
env.close()
def test_reps_cartpole(self):
"""Test REPS with gym Cartpole environment."""
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
env = GymEnv('CartPole-v0')
policy = CategoricalMLPPolicy(env_spec=env.spec,
hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
sampler = LocalSampler(
agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
is_tf_worker=True)
algo = REPS(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99)
trainer.setup(algo, env)
last_avg_ret = trainer.train(n_epochs=10, batch_size=4000)
assert last_avg_ret > 5
env.close()
def run_task(*_):
"""Wrap REPS training task in the run_task function."""
env = TfEnv(gym.make("CartPole-v0"))
policy = CategoricalMLPPolicy(env_spec=env.spec, hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = REPS(env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=100,
discount=0.99,
plot=False)
algo.train()
def test_reps_cartpole(self):
"""Test REPS with gym Cartpole environment."""
logger.reset()
env = TfEnv(gym.make("CartPole-v0"))
policy = CategoricalMLPPolicy(env_spec=env.spec, hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = REPS(env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=10,
discount=0.99,
max_kl_step=1e6,
plot=False)
last_avg_ret = algo.train(sess=self.sess)
assert last_avg_ret > 5
env.close()
def run_task(*_):
with LocalRunner() as runner:
env = TfEnv(gym.make("CartPole-v0"))
policy = CategoricalMLPPolicy(env_spec=env.spec, hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = REPS(env=env,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99)
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=4000, plot=False)
def run_task(snapshot_config, *_):
"""Run task."""
with LocalTFRunner(snapshot_config=snapshot_config) as runner:
env = TfEnv(gym.make('CartPole-v0'))
policy = CategoricalMLPPolicy(env_spec=env.spec, hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = REPS(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99)
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=4000, plot=False)
def test_reps_cartpole(self):
"""Test REPS with gym Cartpole environment."""
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
env = GymEnv('CartPole-v0')
policy = CategoricalMLPPolicy(env_spec=env.spec,
hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = REPS(env_spec=env.spec,
policy=policy,
baseline=baseline,
discount=0.99)
runner.setup(algo, env, sampler_cls=LocalSampler)
last_avg_ret = runner.train(n_epochs=10, batch_size=4000)
assert last_avg_ret > 5
env.close()
def test_on_policy_vectorized_sampler_n_envs(self, cpus, n_envs,
expected_n_envs):
with LocalTFRunner(snapshot_config, sess=self.sess,
max_cpus=cpus) as runner:
env = TfEnv(gym.make('CartPole-v0'))
policy = CategoricalMLPPolicy(env_spec=env.spec,
hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = REPS(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99)
runner.setup(algo, env, sampler_args=dict(n_envs=n_envs))
assert isinstance(runner._sampler, OnPolicyVectorizedSampler)
assert runner._sampler._n_envs == expected_n_envs
env.close()