def test_rl2_sampler_less_envs_than_meta_batch(self):
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
policy = GaussianMLPPolicy(env_spec=self.env.spec,
hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=self.env.spec)
algo = PPO(env_spec=self.env.spec,
policy=policy,
baseline=baseline,
max_path_length=self.max_path_length,
discount=0.99)
runner.setup(algo,
env=self.env,
sampler_cls=RL2Sampler,
sampler_args=dict(
meta_batch_size=self.meta_batch_size,
n_envs=self.meta_batch_size // 2))
runner._start_worker()
assert isinstance(runner._sampler, RL2Sampler)
assert runner._sampler._envs_per_worker == 1
all_indices = np.arange(self.meta_batch_size)
for i in range(self.meta_batch_size // 2):
assert all(runner._sampler._vec_envs_indices[i] ==
all_indices[i * 2:i * 2 + 2])
paths = runner._sampler.obtain_samples(0)
assert len(paths) == self.meta_batch_size
assert len(paths[0]['observations']) == self.max_path_length
paths = runner._sampler.obtain_samples(
0, self.meta_batch_size * 10 * self.max_path_length)
assert len(paths) == self.meta_batch_size * 10
assert len(paths[0]['observations']) == self.max_path_length
def test_ppo_pendulum_gru(self):
"""Test PPO with Pendulum environment and recurrent policy."""
with LocalTFRunner(snapshot_config) as runner:
env = MetaRLEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
gru_policy = GaussianGRUPolicy(env_spec=env.spec)
baseline = GaussianMLPBaseline(
env_spec=env.spec,
regressor_args=dict(hidden_sizes=(32, 32)),
)
algo = PPO(
env_spec=env.spec,
policy=gru_policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False,
)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 80
def ppo_cmb(env, seed, log_dir):
"""Create test continuous mlp baseline on ppo.
Args:
env (gym_env): Environment of the task.
seed (int): Random seed for the trial.
log_dir (str): Log dir path.
Returns:
str: training results in csv format.
"""
deterministic.set_seed(seed)
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=num_proc,
inter_op_parallelism_threads=num_proc)
sess = tf.Session(config=config)
with LocalTFRunner(snapshot_config, sess=sess,
max_cpus=num_proc) as runner:
env = TfEnv(normalize(env))
policy = GaussianLSTMPolicy(
env_spec=env.spec,
hidden_dim=policy_params['policy_hidden_sizes'],
hidden_nonlinearity=policy_params['hidden_nonlinearity'],
)
baseline = ContinuousMLPBaseline(
env_spec=env.spec,
regressor_args=baseline_params['regressor_args'],
)
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=algo_params['max_path_length'],
discount=algo_params['discount'],
gae_lambda=algo_params['gae_lambda'],
lr_clip_range=algo_params['lr_clip_range'],
entropy_method=algo_params['entropy_method'],
policy_ent_coeff=algo_params['policy_ent_coeff'],
optimizer_args=algo_params['optimizer_args'],
center_adv=algo_params['center_adv'],
stop_entropy_gradient=True)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo,
env,
sampler_args=dict(n_envs=algo_params['n_envs']))
runner.train(n_epochs=algo_params['n_epochs'],
batch_size=algo_params['n_rollout_steps'])
dowel_logger.remove_all()
return tabular_log_file
def test_ppo_pendulum_flatten_input(self):
"""Test PPO with CartPole to test observation flattening."""
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
env = MetaRLEnv(
normalize(ReshapeObservation(gym.make('CartPole-v1'), (2, 2))))
policy = CategoricalMLPPolicy(
env_spec=env.spec,
hidden_nonlinearity=tf.nn.tanh,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
learning_rate=1e-3,
))
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 80
def gaussian_gru_policy(ctxt, env_id, seed):
"""Create Gaussian GRU Policy on TF-PPO.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner 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 LocalTFRunner(ctxt) as runner:
env = MetaRLEnv(normalize(gym.make(env_id)))
policy = GaussianGRUPolicy(
env_spec=env.spec,
hidden_dim=32,
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = GaussianMLPBaseline(
env_spec=env.spec,
regressor_args=dict(
hidden_sizes=(64, 64),
use_trust_region=False,
optimizer=FirstOrderOptimizer,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
learning_rate=1e-3,
),
),
)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
learning_rate=1e-3,
),
)
runner.setup(algo, env, sampler_args=dict(n_envs=12))
runner.train(n_epochs=5, batch_size=2048)
def ppo_memorize_digits(ctxt=None, seed=1, batch_size=4000):
"""Train PPO on MemorizeDigits-v0 environment.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner 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 LocalTFRunner(ctxt) as runner:
env = MetaRLEnv(normalize(gym.make('MemorizeDigits-v0')),
is_image=True)
policy = CategoricalCNNPolicy(env_spec=env.spec,
filters=(
(32, (5, 5)),
(64, (3, 3)),
(64, (2, 2)),
),
strides=(4, 2, 1),
padding='VALID',
hidden_sizes=(256, )) # yapf: disable
baseline = GaussianCNNBaseline(
env_spec=env.spec,
regressor_args=dict(filters=(
(32, (5, 5)),
(64, (3, 3)),
(64, (2, 2)),
),
strides=(4, 2, 1),
padding='VALID',
hidden_sizes=(256, ),
use_trust_region=True)) # yapf: disable
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
learning_rate=1e-3,
),
flatten_input=False)
runner.setup(algo, env)
runner.train(n_epochs=1000, batch_size=batch_size)
def ppo_pendulum(ctxt=None, seed=1):
"""Train PPO with InvertedDoublePendulum-v2 environment.
Args:
ctxt (metarl.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 = MetaRLEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = GaussianMLPBaseline(
env_spec=env.spec,
regressor_args=dict(
hidden_sizes=(32, 32),
use_trust_region=True,
),
)
# NOTE: make sure when setting entropy_method to 'max', set
# center_adv to False and turn off policy gradient. See
# tf.algos.NPO for detailed documentation.
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False,
)
runner.setup(algo, env)
runner.train(n_epochs=120, batch_size=2048, plot=False)
def run_metarl(env, seed, log_dir):
'''
Create metarl model and training.
Replace the ppo with the algorithm you want to run.
:param env: Environment of the task.
:param seed: Random seed for the trial.
:param log_dir: Log dir path.
:return:
'''
deterministic.set_seed(seed)
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=12,
inter_op_parallelism_threads=12)
sess = tf.Session(config=config)
with LocalTFRunner(snapshot_config, sess=sess, max_cpus=12) as runner:
env = TfEnv(normalize(env))
policy = CategoricalLSTMPolicy(
env_spec=env.spec,
hidden_dim=32,
hidden_nonlinearity=tf.nn.tanh,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
tf_optimizer_args=dict(learning_rate=1e-3),
),
)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env, sampler_args=dict(n_envs=12))
runner.train(n_epochs=488, batch_size=2048)
dowel_logger.remove_all()
return tabular_log_file
def categorical_cnn_policy(ctxt, env_id, seed):
"""Create Categorical CNN Policy on TF-PPO.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner 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 LocalTFRunner(ctxt, max_cpus=12) as runner:
env = MetaRLEnv(normalize(gym.make(env_id)))
policy = CategoricalCNNPolicy(
env_spec=env.spec,
conv_filters=hyper_params['conv_filters'],
conv_strides=hyper_params['conv_strides'],
conv_pad=hyper_params['conv_pad'],
hidden_sizes=hyper_params['hidden_sizes'])
baseline = GaussianCNNBaseline(
env_spec=env.spec,
regressor_args=dict(
filters=hyper_params['conv_filters'],
strides=hyper_params['conv_strides'],
padding=hyper_params['conv_pad'],
hidden_sizes=hyper_params['hidden_sizes'],
use_trust_region=hyper_params['use_trust_region']))
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
learning_rate=1e-3,
),
flatten_input=False,
)
runner.setup(algo, env)
runner.train(n_epochs=hyper_params['n_epochs'],
batch_size=hyper_params['batch_size'])
def test_ppo_pendulum(self):
"""Test PPO with Pendulum environment."""
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
algo = PPO(env_spec=self.env.spec,
policy=self.policy,
baseline=self.baseline,
max_path_length=100,
discount=0.99,
lr_clip_range=0.01,
optimizer_args=dict(batch_size=32, max_epochs=10))
runner.setup(algo, self.env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 35
def ppo_ml1(ctxt=None, seed=1):
"""Run task."""
set_seed(seed)
with LocalTFRunner(snapshot_config=ctxt) as runner:
Ml1_reach_envs = get_ML1_envs_test(env_id)
env = MTMetaWorldWrapper(Ml1_reach_envs)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
hidden_w_init=tf.constant_initializer(np.sqrt(2)),
hidden_b_init=tf.constant_initializer(np.sqrt(2)),
)
# baseline = LinearFeatureBaseline(env_spec=env.spec)
baseline = GaussianMLPBaseline(
env_spec=env.spec,
regressor_args=dict(
hidden_sizes=(64, 64),
use_trust_region=False,
hidden_w_init=tf.constant_initializer(np.sqrt(2)),
hidden_b_init=tf.constant_initializer(np.sqrt(2)),
),
)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=150,
discount=0.99,
gae_lambda=0.97,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=30,
max_epochs=4,
tf_optimizer_args=dict(learning_rate=3e-4, ),
),
)
timesteps = 6000000
batch_size = 150 * env.num_tasks
epochs = timesteps // batch_size
print(f'epochs: {epochs}, batch_size: {batch_size}')
runner.setup(algo, env, sampler_args={'n_envs': 1})
runner.train(n_epochs=epochs, batch_size=batch_size, plot=False)
def run_task(self, snapshot_config, *_):
config = tf.ConfigProto(device_count={'GPU': 0},
allow_soft_placement=True,
intra_op_parallelism_threads=12,
inter_op_parallelism_threads=12)
sess = tf.Session(config=config)
with LocalTFRunner(snapshot_config=snapshot_config,
sess=sess) as runner:
env = gym.make(self._env)
env = TfEnv(normalize(env))
env.reset()
policy = GaussianGRUPolicy(
env_spec=env.spec,
hidden_dim=32,
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = GaussianMLPBaseline(
env_spec=env.spec,
regressor_args=dict(
hidden_sizes=(64, 64),
use_trust_region=False,
optimizer=FirstOrderOptimizer,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
tf_optimizer_args=dict(learning_rate=1e-3),
),
),
)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
tf_optimizer_args=dict(learning_rate=1e-3),
),
)
runner.setup(algo, env, sampler_args=dict(n_envs=12))
runner.train(n_epochs=5, batch_size=2048)
def continuous_mlp_baseline(ctxt, env_id, seed):
"""Create Continuous MLP Baseline on TF-PPO.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner 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 LocalTFRunner(ctxt, max_cpus=hyper_params['num_proc']) as runner:
env = MetaRLEnv(normalize(gym.make(env_id)))
policy = GaussianLSTMPolicy(
env_spec=env.spec,
hidden_dim=hyper_params['policy_hidden_sizes'],
hidden_nonlinearity=hyper_params['hidden_nonlinearity'],
)
baseline = ContinuousMLPBaseline(
env_spec=env.spec,
regressor_args=dict(hidden_sizes=(64, 64)),
)
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=hyper_params['max_path_length'],
discount=hyper_params['discount'],
gae_lambda=hyper_params['gae_lambda'],
lr_clip_range=hyper_params['lr_clip_range'],
entropy_method=hyper_params['entropy_method'],
policy_ent_coeff=hyper_params['policy_ent_coeff'],
optimizer_args=dict(
batch_size=32,
max_epochs=10,
learning_rate=1e-3,
),
center_adv=hyper_params['center_adv'],
stop_entropy_gradient=True)
runner.setup(algo,
env,
sampler_args=dict(n_envs=hyper_params['n_envs']))
runner.train(n_epochs=hyper_params['n_epochs'],
batch_size=hyper_params['n_rollout_steps'])
def test_ppo_with_maximum_entropy(self):
"""Test PPO with maxium entropy method."""
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
algo = PPO(env_spec=self.env.spec,
policy=self.policy,
baseline=self.baseline,
max_path_length=100,
discount=0.99,
lr_clip_range=0.01,
optimizer_args=dict(batch_size=32, max_epochs=10),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False)
runner.setup(algo, self.env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 35
def ppo_mt10_sampling(ctxt=None, seed=1):
"""Run task."""
set_seed(seed)
with LocalTFRunner(snapshot_config=ctxt) as runner:
env = MultiEnvSamplingWrapper(MT10_envs, env_ids, len(env_ids)-skip_size, sample_strategy=round_robin_strategy)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = GaussianMLPBaseline(
env_spec=env.spec,
regressor_args=dict(
hidden_sizes=(64, 64),
use_trust_region=False,
),
)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=150,
discount=0.99,
gae_lambda=0.97,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
tf_optimizer_args=dict(
learning_rate=3e-4,
),
),
)
batch_size = (len(env_ids)-skip_size)*10*150
epochs = (total_steps//batch_size)+10
print ("epochs:", epochs, "batch_size:", batch_size)
runner.setup(algo, env)
runner.train(n_epochs=epochs, batch_size=batch_size, plot=False)
def categorical_gru_policy(ctxt, env_id, seed):
"""Create Categorical CNN Policy on TF-PPO.
Args:
ctxt (metarl.experiment.ExperimentContext): The experiment
configuration used by LocalRunner 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 LocalTFRunner(ctxt, max_cpus=12) as runner:
env = MetaRLEnv(normalize(gym.make(env_id)))
policy = CategoricalGRUPolicy(
env_spec=env.spec,
hidden_dim=32,
hidden_nonlinearity=tf.nn.tanh,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
learning_rate=1e-3,
),
)
runner.setup(algo, env, sampler_args=dict(n_envs=12))
runner.train(n_epochs=488, batch_size=2048)
def test_ppo_with_neg_log_likeli_entropy_estimation_and_regularized(self):
"""
Test PPO with negative log likelihood entropy estimation and
regularized entropy method.
"""
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
algo = PPO(env_spec=self.env.spec,
policy=self.policy,
baseline=self.baseline,
max_path_length=100,
discount=0.99,
lr_clip_range=0.01,
optimizer_args=dict(batch_size=32, max_epochs=10),
stop_entropy_gradient=True,
use_neg_logli_entropy=True,
entropy_method='regularized',
policy_ent_coeff=0.0,
center_adv=True)
runner.setup(algo, self.env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 35
def run_task(snapshot_config, variant_data, *_):
"""Run task.
Args:
snapshot_config (metarl.experiment.SnapshotConfig): The snapshot
configuration used by LocalRunner to create the snapshotter.
variant_data (dict): Custom arguments for the task.
*_ (object): Ignored by this function.
"""
with LocalTFRunner(snapshot_config=snapshot_config) as runner:
env = TfEnv(normalize(gym.make('MemorizeDigits-v0')))
policy = CategoricalCNNPolicy(env_spec=env.spec,
conv_filters=(32, 64, 64),
conv_filter_sizes=(5, 3, 2),
conv_strides=(4, 2, 1),
conv_pad='VALID',
hidden_sizes=(256, ))
baseline = GaussianCNNBaseline(env_spec=env.spec,
regressor_args=dict(
num_filters=(32, 64, 64),
filter_dims=(5, 3, 2),
strides=(4, 2, 1),
padding='VALID',
hidden_sizes=(256, ),
use_trust_region=True))
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
max_kl_step=0.01,
flatten_input=False)
runner.setup(algo, env)
runner.train(n_epochs=1000, batch_size=variant_data['batch_size'])
def multi_env_ppo(ctxt=None, seed=1):
"""Train PPO on two Atari environments simultaneously.
Args:
ctxt (metarl.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:
env1 = MetaRLEnv(normalize(gym.make('Adventure-ram-v4')))
env2 = MetaRLEnv(normalize(gym.make('Alien-ram-v4')))
env = MultiEnvWrapper([env1, env2])
policy = CategoricalMLPPolicy(
env_spec=env.spec,
hidden_nonlinearity=tf.nn.tanh,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
learning_rate=1e-3,
))
runner.setup(algo, env)
runner.train(n_epochs=120, batch_size=2048, plot=False)
def run_task(snapshot_config, *_):
"""Run task.
Args:
snapshot_config (metarl.experiment.SnapshotConfig): The snapshot
configuration used by LocalRunner to create the snapshotter.
_ (object): Ignored by this function.
"""
with LocalTFRunner(snapshot_config=snapshot_config) as runner:
env1 = TfEnv(normalize(gym.make('Adventure-ram-v4')))
env2 = TfEnv(normalize(gym.make('Alien-ram-v4')))
env = MultiEnvWrapper([env1, env2])
policy = CategoricalMLPPolicy(
env_spec=env.spec,
hidden_nonlinearity=tf.nn.tanh,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
tf_optimizer_args=dict(learning_rate=1e-3),
))
runner.setup(algo, env)
runner.train(n_epochs=120, batch_size=2048, plot=False)
def test_rl2_sampler_invalid_num_of_env_again(self):
with pytest.raises(
ValueError,
match='n_envs must be a multiple of meta_batch_size'):
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
policy = GaussianMLPPolicy(env_spec=self.env.spec,
hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=self.env.spec)
algo = PPO(env_spec=self.env.spec,
policy=policy,
baseline=baseline,
max_path_length=self.max_path_length,
discount=0.99)
runner.setup(algo,
env=self.env,
sampler_cls=RL2Sampler,
sampler_args=dict(
meta_batch_size=self.meta_batch_size,
n_envs=self.meta_batch_size + 1))
runner._start_worker()
runner._sampler.obtain_samples(0)
def test_ppo_pendulum_continuous_baseline(self):
"""Test PPO with Pendulum environment."""
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
env = MetaRLEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = ContinuousMLPBaseline(
env_spec=env.spec,
regressor_args=dict(hidden_sizes=(32, 32)),
)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False,
)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 100
env.close()
def run_metarl(env, seed, log_dir):
'''
Create metarl model and training.
Replace the ppo with the algorithm you want to run.
:param env: Environment of the task.
:param seed: Random seed for the trial.
:param log_dir: Log dir path.
:return:
'''
deterministic.set_seed(seed)
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=12,
inter_op_parallelism_threads=12)
sess = tf.Session(config=config)
with LocalTFRunner(snapshot_config, sess=sess, max_cpus=12) as runner:
env = TfEnv(normalize(env))
policy = CategoricalCNNPolicy(
env_spec=env.spec,
conv_filters=params['conv_filters'],
conv_filter_sizes=params['conv_filter_sizes'],
conv_strides=params['conv_strides'],
conv_pad=params['conv_pad'],
hidden_sizes=params['hidden_sizes'])
baseline = GaussianCNNBaseline(
env_spec=env.spec,
regressor_args=dict(num_filters=params['conv_filters'],
filter_dims=params['conv_filter_sizes'],
strides=params['conv_strides'],
padding=params['conv_pad'],
hidden_sizes=params['hidden_sizes'],
use_trust_region=params['use_trust_region']))
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
tf_optimizer_args=dict(learning_rate=1e-3),
),
flatten_input=False,
)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=params['n_epochs'],
batch_size=params['batch_size'])
dowel_logger.remove_all()
return tabular_log_file
