def test_make_sampler_ray_sampler(self, ray_session_fixture):
del ray_session_fixture
assert ray.is_initialized()
with TFTrainer(snapshot_config) as trainer:
env = GymEnv('CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(8, 8))
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 = VPG(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99,
optimizer_args=dict(learning_rate=0.01, ))
trainer.setup(algo, env)
assert isinstance(trainer._sampler, RaySampler)
trainer.train(n_epochs=1, batch_size=10)
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 trpo_garage_tf(ctxt, env_id, seed):
"""Create garage Tensorflow TROI model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
with TFTrainer(ctxt) as trainer:
env = normalize(GymEnv(env_id))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
discount=hyper_parameters['discount'],
gae_lambda=hyper_parameters['gae_lambda'],
max_kl_step=hyper_parameters['max_kl'])
trainer.setup(algo, env)
trainer.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
def trpo_cartpole_recurrent(ctxt, seed, n_epochs, batch_size, plot):
"""Train TRPO with a recurrent policy on CartPole.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
n_epochs (int): Number of epochs for training.
seed (int): Used to seed the random number generator to produce
determinism.
batch_size (int): Batch size used for training.
plot (bool): Whether to plot or not.
"""
set_seed(seed)
with TFTrainer(snapshot_config=ctxt) as trainer:
env = GymEnv('CartPole-v1', max_episode_length=100)
policy = CategoricalLSTMPolicy(name='policy', env_spec=env.spec)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
discount=0.99,
max_kl_step=0.01,
optimizer=ConjugateGradientOptimizer,
optimizer_args=dict(hvp_approach=FiniteDifferenceHVP(
base_eps=1e-5)))
trainer.setup(algo, env)
trainer.train(n_epochs=n_epochs, batch_size=batch_size, plot=plot)
def multi_env_trpo(ctxt=None, seed=1):
"""Train TRPO on two different PointEnv instances.
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(ctxt) as trainer:
env1 = normalize(PointEnv(goal=(-1., 0.), max_episode_length=100))
env2 = normalize(PointEnv(goal=(1., 0.), max_episode_length=100))
env = MultiEnvWrapper([env1, env2])
policy = GaussianMLPPolicy(env_spec=env.spec)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0)
trainer.setup(algo, env)
trainer.train(n_epochs=40, batch_size=2048, plot=False)
def test_ppo_pendulum_gru(self):
"""Test PPO with Pendulum environment and recurrent policy."""
with TFTrainer(snapshot_config) as trainer:
env = normalize(
GymEnv('InvertedDoublePendulum-v2', max_episode_length=100))
gru_policy = GaussianGRUPolicy(env_spec=env.spec)
baseline = GaussianMLPBaseline(
env_spec=env.spec,
hidden_sizes=(32, 32),
)
algo = PPO(
env_spec=env.spec,
policy=gru_policy,
baseline=baseline,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False,
)
trainer.setup(algo, env, sampler_cls=LocalSampler)
last_avg_ret = trainer.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 80
def test_erwr_cartpole(self):
"""Test ERWR with Cartpole-v1 environment."""
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
deterministic.set_seed(1)
env = GymEnv('CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
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 = ERWR(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=10000)
assert last_avg_ret > 60
env.close()
def train_gru_trpo(ctxt=None):
set_seed(seed)
with TFTrainer(snapshot_config=ctxt) as trainer:
env = MyGymEnv(gym_env, max_episode_length=100)
policy = CategoricalGRUPolicy(name='policy',
env_spec=env.spec,
state_include_action=False)
baseline = LinearFeatureBaseline(env_spec=env.spec)
sampler = LocalSampler(
agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
worker_class=FragmentWorker,
)
self.algo = LoggedTRPO(
env=env,
env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99,
max_kl_step=0.01,
optimizer_args=dict(hvp_approach=FiniteDifferenceHVP(
base_eps=1e-5)))
trainer.setup(self.algo, env)
trainer.train(n_epochs=n_eps, batch_size=4000)
return self.algo.rew_chkpts
def test_trpo_lstm_cartpole(self):
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
env = normalize(GymEnv('CartPole-v1', max_episode_length=100))
policy = CategoricalLSTMPolicy(name='policy', env_spec=env.spec)
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 = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99,
max_kl_step=0.01,
optimizer_args=dict(hvp_approach=FiniteDifferenceHVP(
base_eps=1e-5)))
snapshotter.snapshot_dir = './'
trainer.setup(algo, env)
last_avg_ret = trainer.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 60
env.close()
def fixture_exp(snapshot_config, sess):
"""Dummy fixture experiment function.
Args:
snapshot_config (garage.experiment.SnapshotConfig): The snapshot
configuration used by Trainer to create the snapshotter.
If None, it will create one with default settings.
sess (tf.Session): An optional TensorFlow session.
A new session will be created immediately if not provided.
Returns:
np.ndarray: Values of the parameters evaluated in
the current session
"""
with TFTrainer(snapshot_config=snapshot_config, sess=sess) as trainer:
env = GymEnv('CartPole-v1', max_episode_length=100)
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(8, 8))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = VPG(env_spec=env.spec,
policy=policy,
baseline=baseline,
discount=0.99,
optimizer_args=dict(learning_rate=0.01, ))
trainer.setup(algo, env, sampler_cls=LocalSampler)
trainer.train(n_epochs=5, batch_size=100)
return policy.get_param_values()
def test_te_ppo(self):
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
sampler = LocalSampler(
agents=self.policy,
envs=self.env,
max_episode_length=self.env.spec.max_episode_length,
is_tf_worker=True,
worker_class=TaskEmbeddingWorker)
algo = TEPPO(env_spec=self.env.spec,
policy=self.policy,
baseline=self.baseline,
inference=self.inference,
sampler=sampler,
discount=0.99,
lr_clip_range=0.2,
policy_ent_coeff=self.policy_ent_coeff,
encoder_ent_coeff=self.encoder_ent_coeff,
inference_ce_coeff=self.inference_ce_coeff,
use_softplus_entropy=True,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
),
inference_optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
),
center_adv=True,
stop_ce_gradient=True)
trainer.setup(algo, self.env)
trainer.train(n_epochs=1, batch_size=self.batch_size, plot=False)
def test_ddpg_double_pendulum(self):
"""Test DDPG with Pendulum environment."""
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
env = GymEnv('InvertedDoublePendulum-v2')
policy = ContinuousMLPPolicy(env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh)
exploration_policy = AddOrnsteinUhlenbeckNoise(env.spec,
policy,
sigma=0.2)
qf = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu)
replay_buffer = PathBuffer(capacity_in_transitions=int(1e5))
algo = DDPG(
env_spec=env.spec,
policy=policy,
policy_lr=1e-4,
qf_lr=1e-3,
qf=qf,
replay_buffer=replay_buffer,
steps_per_epoch=20,
target_update_tau=1e-2,
n_train_steps=50,
discount=0.9,
min_buffer_size=int(5e3),
exploration_policy=exploration_policy,
)
trainer.setup(algo, env)
last_avg_ret = trainer.train(n_epochs=10, batch_size=100)
assert last_avg_ret > 60
env.close()
def test_set_plot(self):
deterministic.set_seed(1)
with TFTrainer(snapshot_config) as trainer:
env = GymEnv('CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(8, 8))
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 = VPG(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99,
optimizer_args=dict(learning_rate=0.01, ))
trainer.setup(algo, env)
trainer.train(n_epochs=1, batch_size=100, plot=True)
assert isinstance(trainer._plotter, Plotter), (
'self.plotter in TFTrainer should be set to Plotter.')
def test_train(self):
with TFTrainer(snapshot_config) as trainer:
env = GymEnv('CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(8, 8))
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 = VPG(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99,
optimizer_args=dict(learning_rate=0.01, ))
trainer.setup(algo, env)
trainer.train(n_epochs=1, batch_size=100)
def train_ppo(ctxt=None):
set_seed(seed)
with TFTrainer(ctxt) as trainer:
env = MyGymEnv(gym_env, max_episode_length=100)
policy = CategoricalGRUPolicy(name='policy',
env_spec=env.spec,
state_include_action=False)
baseline = LinearFeatureBaseline(env_spec=env.spec)
sampler = LocalSampler(
agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
worker_class=FragmentWorker,
is_tf_worker=True,
)
self.algo = LoggedPPO(
env=env,
env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99,
center_adv=False,
optimizer_args=dict(max_optimization_epochs=8))
trainer.setup(self.algo, env)
trainer.train(n_epochs=n_eps, batch_size=4000)
return self.algo.rew_chkpts
def test_rl2_ppo_pendulum_meta_test(self):
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
meta_evaluator = MetaEvaluator(test_task_sampler=self.tasks,
n_exploration_eps=10,
n_test_episodes=10,
n_test_tasks=1)
algo = RL2PPO(meta_batch_size=self.meta_batch_size,
task_sampler=self.tasks,
env_spec=self.env_spec,
policy=self.policy,
baseline=self.baseline,
sampler=self.sampler,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False,
episodes_per_trial=self.episode_per_task,
meta_evaluator=meta_evaluator,
n_epochs_per_eval=10)
trainer.setup(algo, self.tasks.sample(self.meta_batch_size))
last_avg_ret = trainer.train(n_epochs=1,
batch_size=self.episode_per_task *
self.max_episode_length *
self.meta_batch_size)
assert last_avg_ret > -40
def test_rl2_trpo_pendulum(self):
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
algo = RL2TRPO(
meta_batch_size=self.meta_batch_size,
task_sampler=self.tasks,
env_spec=self.env_spec,
policy=self.policy,
baseline=self.baseline,
episodes_per_trial=self.episode_per_task,
discount=0.99,
max_kl_step=0.01,
optimizer=ConjugateGradientOptimizer,
optimizer_args=dict(hvp_approach=FiniteDifferenceHVP(
base_eps=1e-5)))
trainer.setup(algo,
self.tasks.sample(self.meta_batch_size),
sampler_cls=LocalSampler,
n_workers=self.meta_batch_size,
worker_class=RL2Worker)
last_avg_ret = trainer.train(n_epochs=1,
batch_size=self.episode_per_task *
self.max_episode_length *
self.meta_batch_size)
assert last_avg_ret > -40
def rl2_ppo_halfcheetah(ctxt, seed, max_episode_length, meta_batch_size,
n_epochs, episode_per_task):
"""Train PPO with HalfCheetah environment.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
max_episode_length (int): Maximum length of a single episode.
meta_batch_size (int): Meta batch size.
n_epochs (int): Total number of epochs for training.
episode_per_task (int): Number of training episode per task.
"""
set_seed(seed)
with TFTrainer(snapshot_config=ctxt) as trainer:
tasks = task_sampler.SetTaskSampler(
HalfCheetahVelEnv,
wrapper=lambda env, _: RL2Env(
GymEnv(env, max_episode_length=max_episode_length)))
env_spec = RL2Env(
GymEnv(HalfCheetahVelEnv(),
max_episode_length=max_episode_length)).spec
policy = GaussianGRUPolicy(name='policy',
hidden_dim=64,
env_spec=env_spec,
state_include_action=False)
baseline = LinearFeatureBaseline(env_spec=env_spec)
algo = RL2PPO(meta_batch_size=meta_batch_size,
task_sampler=tasks,
env_spec=env_spec,
policy=policy,
baseline=baseline,
episodes_per_trial=episode_per_task,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False)
trainer.setup(algo,
tasks.sample(meta_batch_size),
sampler_cls=LocalSampler,
n_workers=meta_batch_size,
worker_class=RL2Worker,
worker_args=dict(n_episodes_per_trial=episode_per_task))
trainer.train(n_epochs=n_epochs,
batch_size=episode_per_task * max_episode_length *
meta_batch_size)
def trpo_cartpole_bullet(ctxt=None, seed=1):
"""Train TRPO with Pybullet's CartPoleBulletEnv 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(ctxt) as trainer:
env = BulletEnv(
gym.make('CartPoleBulletEnv-v1',
renders=False,
discrete_actions=True))
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,
discount=0.99,
max_kl_step=0.01)
trainer.setup(algo, env)
trainer.train(n_epochs=100, batch_size=4000)
def test_cem_cartpole(self):
"""Test CEM with Cartpole-v1 environment."""
with TFTrainer(snapshot_config) as trainer:
env = GymEnv('CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
n_samples = 10
sampler = LocalSampler(
agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
is_tf_worker=True)
algo = CEM(env_spec=env.spec,
policy=policy,
sampler=sampler,
best_frac=0.1,
n_samples=n_samples)
trainer.setup(algo, env)
rtn = trainer.train(n_epochs=10, batch_size=2048)
assert rtn > 40
env.close()
def trpo_swimmer(ctxt=None, seed=1, batch_size=4000):
"""Train TRPO with Swimmer-v2 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.
batch_size (int): Number of timesteps to use in each training step.
"""
set_seed(seed)
with TFTrainer(ctxt) as trainer:
env = GymEnv('Swimmer-v2')
policy = GaussianMLPPolicy(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 = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99,
max_kl_step=0.01)
trainer.setup(algo, env)
trainer.train(n_epochs=40, batch_size=batch_size)
def cma_es_cartpole(ctxt=None, seed=1):
"""Train CMA_ES with Cartpole-v1 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(ctxt) as trainer:
env = GymEnv('CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
n_samples = 20
sampler = LocalSampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
is_tf_worker=True)
algo = CMAES(env_spec=env.spec,
policy=policy,
sampler=sampler,
n_samples=n_samples)
trainer.setup(algo, env)
trainer.train(n_epochs=100, batch_size=1000)
def test_dm_control_tf_policy(self):
task = ALL_TASKS[0]
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
env = DMControlEnv.from_suite(*task)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(32, 32),
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
discount=0.99,
max_kl_step=0.01,
)
trainer.setup(algo, env, sampler_cls=LocalSampler)
trainer.train(n_epochs=1, batch_size=10)
env.close()
def test_rl2_ppo_pendulum_adapted_policy(self):
with TFTrainer(snapshot_config, sess=self.sess):
algo = RL2PPO(meta_batch_size=self.meta_batch_size,
task_sampler=self.tasks,
env_spec=self.env_spec,
policy=self.policy,
baseline=self.baseline,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False,
episodes_per_trial=self.episode_per_task)
exploration_policy = algo.get_exploration_policy()
adapted_policy = algo.adapt_policy(exploration_policy, [])
(params, hidden) = adapted_policy.get_param_values()
expected_new_params = np.zeros_like(params)
expected_hidden = np.zeros_like(hidden)
adapted_policy.set_param_values(
(expected_new_params, expected_hidden))
(new_params, new_hidden) = adapted_policy.get_param_values()
assert np.array_equal(expected_new_params, new_params)
assert np.array_equal(expected_hidden, new_hidden)
def cem_cartpole(ctxt=None, seed=1):
"""Train CEM with Cartpole-v1 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-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
n_samples = 20
algo = CEM(env_spec=env.spec,
policy=policy,
best_frac=0.05,
n_samples=n_samples)
trainer.setup(algo, env)
trainer.train(n_epochs=100, batch_size=1000)
def test_rl2_ppo_pendulum_wrong_worker(self):
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
with pytest.raises(ValueError):
algo = RL2PPO(meta_batch_size=self.meta_batch_size,
task_sampler=self.tasks,
env_spec=self.env_spec,
policy=self.policy,
baseline=self.baseline,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False,
episodes_per_trial=self.episode_per_task)
trainer.setup(algo,
self.tasks.sample(self.meta_batch_size),
sampler_cls=LocalSampler,
n_workers=self.meta_batch_size)
trainer.train(n_epochs=10,
batch_size=self.episode_per_task *
self.max_episode_length * self.meta_batch_size)
def trpo_gym_tf_cartpole(ctxt=None, seed=1):
"""Train TRPO 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-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,
discount=0.99,
max_kl_step=0.01,
)
trainer.setup(algo, env)
trainer.train(n_epochs=10, batch_size=10000, plot=False)
def test_rl2_ppo_pendulum(self):
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
algo = RL2PPO(meta_batch_size=self.meta_batch_size,
task_sampler=self.tasks,
env_spec=self.env_spec,
policy=self.policy,
baseline=self.baseline,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False,
episodes_per_trial=self.episode_per_task)
trainer.setup(
algo,
self.tasks.sample(self.meta_batch_size),
sampler_cls=LocalSampler,
n_workers=self.meta_batch_size,
worker_class=RL2Worker,
worker_args=dict(n_episodes_per_trial=self.episode_per_task))
last_avg_ret = trainer.train(n_epochs=1,
batch_size=self.episode_per_task *
self.max_episode_length *
self.meta_batch_size)
assert last_avg_ret > -40
def vpg_cartpole(ctxt=None, seed=1):
"""Train VPG with CartPole-v1 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-v1')
policy = CategoricalMLPPolicy(name='policy',
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 = VPG(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99,
optimizer_args=dict(learning_rate=0.01, ))
trainer.setup(algo, env)
trainer.train(n_epochs=100, batch_size=10000)
def test_gaussian_policies(self, policy_cls):
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
env = normalize(GymEnv('Pendulum-v0'))
policy = policy_cls(name='policy', env_spec=env.spec)
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 = TRPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
discount=0.99,
max_kl_step=0.01,
optimizer=ConjugateGradientOptimizer,
optimizer_args=dict(hvp_approach=FiniteDifferenceHVP(
base_eps=1e-5)),
)
trainer.setup(algo, env)
trainer.train(n_epochs=1, batch_size=4000)
env.close()
