def test_fit_without_trusted_region(self):
box_env_spec = GymEnv(DummyBoxEnv(obs_dim=(2, ))).spec
gmb = GaussianMLPBaseline(env_spec=box_env_spec,
use_trust_region=False)
train_paths, _, _, paths, expected = get_train_test_data()
for _ in range(150):
gmb.fit(train_paths)
prediction = gmb.predict(paths)
assert np.allclose(prediction, expected, rtol=0, atol=0.1)
def setup_method(self):
super().setup_method()
self.env = normalize(GymEnv('InvertedDoublePendulum-v2'))
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
self.baseline = GaussianMLPBaseline(
env_spec=self.env.spec,
hidden_sizes=(32, 32),
)
def setup_method(self):
"""Setup method which is called before every test."""
self.env = normalize(GymEnv(HalfCheetahDirEnv(),
max_episode_length=100),
expected_action_scale=10.)
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
self.value_function = GaussianMLPValueFunction(env_spec=self.env.spec,
hidden_sizes=(32, 32))
def test_output_shape(batch_size, hidden_sizes):
env_spec = GymEnv(DummyBoxEnv()).spec
obs_dim = env_spec.observation_space.flat_dim
obs = torch.ones(batch_size, obs_dim, dtype=torch.float32)
qf = DiscreteMLPQFunction(env_spec=env_spec,
hidden_nonlinearity=None,
hidden_sizes=hidden_sizes,
hidden_w_init=nn.init.ones_,
output_w_init=nn.init.ones_)
output = qf(obs)
assert output.shape == (batch_size, env_spec.action_space.flat_dim)
def setup_method(self):
"""Setup method which is called before every test."""
self.env = normalize(GymEnv(HalfCheetahDirEnv(),
max_episode_length=100),
expected_action_scale=10.)
self.task_sampler = SetTaskSampler(
HalfCheetahDirEnv,
wrapper=lambda env, _: normalize(GymEnv(env,
max_episode_length=100),
expected_action_scale=10.))
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
self.value_function = GaussianMLPValueFunction(env_spec=self.env.spec,
hidden_sizes=(32, 32))
self.sampler = LocalSampler(
agents=self.policy,
envs=self.env,
max_episode_length=self.env.spec.max_episode_length)
def continuous_mlp_baseline(ctxt, env_id, seed):
"""Create Continuous MLP Baseline on TF-PPO.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~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 = GaussianLSTMPolicy(
env_spec=env.spec,
hidden_dim=hyper_params['policy_hidden_sizes'],
hidden_nonlinearity=hyper_params['hidden_nonlinearity'],
)
baseline = ContinuousMLPBaseline(
env_spec=env.spec,
hidden_sizes=(64, 64),
)
sampler = RaySampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
is_tf_worker=True)
algo = PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
sampler=sampler,
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_optimization_epochs=10,
learning_rate=1e-3,
),
center_adv=hyper_params['center_adv'],
stop_entropy_gradient=True)
trainer.setup(algo, env)
trainer.train(n_epochs=hyper_params['n_epochs'],
batch_size=hyper_params['n_exploration_steps'])
def test_sac_inverted_double_pendulum():
"""Test Sac performance on inverted pendulum."""
# pylint: disable=unexpected-keyword-arg
env = normalize(GymEnv('InvertedDoublePendulum-v2',
max_episode_length=100))
deterministic.set_seed(0)
policy = TanhGaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=[32, 32],
hidden_nonlinearity=torch.nn.ReLU,
output_nonlinearity=None,
min_std=np.exp(-20.),
max_std=np.exp(2.),
)
qf1 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[32, 32],
hidden_nonlinearity=F.relu)
qf2 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[32, 32],
hidden_nonlinearity=F.relu)
replay_buffer = PathBuffer(capacity_in_transitions=int(1e6), )
runner = LocalRunner(snapshot_config=snapshot_config)
sac = SAC(env_spec=env.spec,
policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=100,
replay_buffer=replay_buffer,
min_buffer_size=1e3,
target_update_tau=5e-3,
discount=0.99,
buffer_batch_size=64,
reward_scale=1.,
steps_per_epoch=2)
runner.setup(sac, env, sampler_cls=LocalSampler)
if torch.cuda.is_available():
set_gpu_mode(True)
else:
set_gpu_mode(False)
sac.to()
ret = runner.train(n_epochs=12, batch_size=200, plot=False)
# check that automatic entropy tuning is used
assert sac._use_automatic_entropy_tuning
# assert that there was a gradient properly connected to alpha
# this doesn't verify that the path from the temperature objective is
# correct.
assert not torch.allclose(torch.Tensor([1.]), sac._log_alpha.to('cpu'))
# check that policy is learning beyond predecided threshold
assert ret > 80
def gaussian_mlp_baseline(ctxt, env_id, seed):
"""Create Gaussian MLP Baseline on TF-PPO.
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=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = GaussianMLPBaseline(
env_spec=env.spec,
hidden_sizes=(64, 64),
use_trust_region=False,
optimizer=FirstOrderOptimizer,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
learning_rate=1e-3,
),
)
algo = PPO(
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,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
learning_rate=1e-3,
),
)
trainer.setup(algo, env, sampler_args=dict(n_envs=12))
trainer.train(n_epochs=5, batch_size=2048)
def test_mtsac_get_log_alpha_incorrect_num_tasks(monkeypatch):
"""Check that if the num_tasks passed does not match the number of tasks
in the environment, then the algorithm should raise an exception.
MTSAC uses disentangled alphas, meaning that
"""
env_names = ['CartPole-v0', 'CartPole-v1']
task_envs = [GymEnv(name, max_episode_length=150) for name in env_names]
env = MultiEnvWrapper(task_envs, sample_strategy=round_robin_strategy)
deterministic.set_seed(0)
policy = TanhGaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=[1, 1],
hidden_nonlinearity=torch.nn.ReLU,
output_nonlinearity=None,
min_std=np.exp(-20.),
max_std=np.exp(2.),
)
qf1 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[1, 1],
hidden_nonlinearity=F.relu)
qf2 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[1, 1],
hidden_nonlinearity=F.relu)
replay_buffer = PathBuffer(capacity_in_transitions=int(1e6), )
buffer_batch_size = 2
mtsac = MTSAC(policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=150,
eval_env=env,
env_spec=env.spec,
num_tasks=4,
steps_per_epoch=5,
replay_buffer=replay_buffer,
min_buffer_size=1e3,
target_update_tau=5e-3,
discount=0.99,
buffer_batch_size=buffer_batch_size)
monkeypatch.setattr(mtsac, '_log_alpha', torch.Tensor([1., 2.]))
error_string = ('The number of tasks in the environment does '
'not match self._num_tasks. Are you sure that you passed '
'The correct number of tasks?')
obs = torch.Tensor([env.reset()[0]] * buffer_batch_size)
with pytest.raises(ValueError, match=error_string):
mtsac._get_log_alpha(dict(observation=obs))
def test_td3_pendulum(self):
"""Test TD3 with Pendulum environment."""
with LocalTFRunner(snapshot_config) as runner:
env = GymEnv('InvertedDoublePendulum-v2', max_episode_length=100)
policy = ContinuousMLPPolicy(env_spec=env.spec,
hidden_sizes=[400, 300],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh)
exploration_policy = AddGaussianNoise(env.spec,
policy,
max_sigma=0.1,
min_sigma=0.1)
qf = ContinuousMLPQFunction(name='ContinuousMLPQFunction',
env_spec=env.spec,
hidden_sizes=[400, 300],
action_merge_layer=0,
hidden_nonlinearity=tf.nn.relu)
qf2 = ContinuousMLPQFunction(name='ContinuousMLPQFunction2',
env_spec=env.spec,
hidden_sizes=[400, 300],
action_merge_layer=0,
hidden_nonlinearity=tf.nn.relu)
replay_buffer = PathBuffer(capacity_in_transitions=int(1e6))
algo = TD3(env_spec=env.spec,
policy=policy,
policy_lr=1e-3,
qf_lr=1e-3,
qf=qf,
qf2=qf2,
replay_buffer=replay_buffer,
steps_per_epoch=20,
target_update_tau=0.005,
n_train_steps=50,
discount=0.99,
min_buffer_size=int(1e4),
buffer_batch_size=100,
policy_weight_decay=0.001,
qf_weight_decay=0.001,
exploration_policy=exploration_policy,
policy_optimizer=tf.compat.v1.train.AdamOptimizer,
qf_optimizer=tf.compat.v1.train.AdamOptimizer)
runner.setup(algo, env, sampler_cls=LocalSampler)
last_avg_ret = runner.train(n_epochs=10, batch_size=250)
assert last_avg_ret > 200
def test_dqn_cartpole_pickle(self):
"""Test DQN with CartPole environment."""
deterministic.set_seed(100)
with TFTrainer(snapshot_config, sess=self.sess) as trainer:
n_epochs = 10
steps_per_epoch = 10
sampler_batch_size = 500
num_timesteps = n_epochs * steps_per_epoch * sampler_batch_size
env = GymEnv('CartPole-v0')
replay_buffer = PathBuffer(capacity_in_transitions=int(1e4))
qf = DiscreteMLPQFunction(env_spec=env.spec, hidden_sizes=(64, 64))
policy = DiscreteQFArgmaxPolicy(env_spec=env.spec, qf=qf)
epilson_greedy_policy = EpsilonGreedyPolicy(
env_spec=env.spec,
policy=policy,
total_timesteps=num_timesteps,
max_epsilon=1.0,
min_epsilon=0.02,
decay_ratio=0.1)
algo = DQN(env_spec=env.spec,
policy=policy,
qf=qf,
exploration_policy=epilson_greedy_policy,
replay_buffer=replay_buffer,
qf_lr=1e-4,
discount=1.0,
min_buffer_size=int(1e3),
double_q=False,
n_train_steps=500,
grad_norm_clipping=5.0,
steps_per_epoch=steps_per_epoch,
target_network_update_freq=1,
buffer_batch_size=32)
trainer.setup(algo, env)
with tf.compat.v1.variable_scope(
'DiscreteMLPQFunction/mlp/hidden_0', reuse=True):
bias = tf.compat.v1.get_variable('bias')
# assign it to all one
old_bias = tf.ones_like(bias).eval()
bias.load(old_bias)
h = pickle.dumps(algo)
with tf.compat.v1.Session(graph=tf.Graph()):
pickle.loads(h)
with tf.compat.v1.variable_scope(
'DiscreteMLPQFunction/mlp/hidden_0', reuse=True):
new_bias = tf.compat.v1.get_variable('bias')
new_bias = new_bias.eval()
assert np.array_equal(old_bias, new_bias)
env.close()
def multi_env_ppo(ctxt=None, seed=1):
"""Train PPO on two Atari environments simultaneously.
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(GymEnv('Adventure-ram-v4'))
env2 = normalize(GymEnv('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,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
policy_ent_coeff=0.0,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
learning_rate=1e-3,
))
trainer.setup(algo, env)
trainer.train(n_epochs=120, batch_size=2048, plot=False)
def test_get_time_limit_finds_time_limit():
env = gym.make('PongNoFrameskip-v4')
time_limit = env._max_episode_steps
env = Noop(env, noop_max=30)
env = MaxAndSkip(env, skip=4)
env = EpisodicLife(env)
env = Grayscale(env)
env = Resize(env, 84, 84)
env = ClipReward(env)
env = StackFrames(env, 4)
env = GymEnv(env)
assert env._max_episode_length == time_limit
def test_get_action():
env_spec = GymEnv(DummyBoxEnv()).spec
obs_dim = env_spec.observation_space.flat_dim
obs = torch.ones([
obs_dim,
], dtype=torch.float32)
qf = DiscreteMLPQFunction(env_spec=env_spec,
hidden_nonlinearity=None,
hidden_sizes=(2, 2))
qvals = qf(obs.unsqueeze(0))
policy = DiscreteQFArgmaxPolicy(qf, env_spec)
action, _ = policy.get_action(obs.numpy())
assert action == torch.argmax(qvals, dim=1).numpy()
assert action.shape == ()
def test_build(self, obs_dim, action_dim):
env = GymEnv(DummyDiscreteEnv(obs_dim=obs_dim, action_dim=action_dim))
qf = DiscreteMLPDuelingQFunction(env_spec=env.spec)
env.reset()
obs = env.step(1).observation
output1 = self.sess.run(qf.q_vals, feed_dict={qf.input: [obs]})
input_var = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + obs_dim)
q_vals = qf.build(input_var, 'another')
output2 = self.sess.run(q_vals, feed_dict={input_var: [obs]})
assert np.array_equal(output1, output2)
def test_get_action(self, hidden_channels, kernel_sizes, strides,
hidden_sizes):
"""Test get_action function."""
env = GymEnv(DummyDiscretePixelEnv(), is_image=True)
policy = CategoricalCNNPolicy(env_spec=env.spec,
image_format='NHWC',
kernel_sizes=kernel_sizes,
hidden_channels=hidden_channels,
strides=strides,
hidden_sizes=hidden_sizes)
env.reset()
obs = env.step(1).observation
action, _ = policy.get_action(obs)
assert env.action_space.contains(action)
def ppo_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow PPO 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 = TF_GMP(
env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
)
baseline = TF_GMB(
env_spec=env.spec,
hidden_sizes=(32, 32),
use_trust_region=False,
optimizer=FirstOrderOptimizer,
optimizer_args=dict(
batch_size=32,
max_optimization_epochs=10,
learning_rate=3e-4,
),
)
algo = TF_PPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
discount=0.99,
gae_lambda=0.95,
center_adv=True,
lr_clip_range=0.2,
optimizer_args=dict(batch_size=32,
max_optimization_epochs=10,
learning_rate=3e-4,
verbose=True))
trainer.setup(algo, env)
trainer.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
def test_mtsac_get_log_alpha(monkeypatch):
"""Check that the private function _get_log_alpha functions correctly.
MTSAC uses disentangled alphas, meaning that
"""
env_names = ['CartPole-v0', 'CartPole-v1']
task_envs = [GymEnv(name) for name in env_names]
env = MultiEnvWrapper(task_envs, sample_strategy=round_robin_strategy)
deterministic.set_seed(0)
policy = TanhGaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=[1, 1],
hidden_nonlinearity=torch.nn.ReLU,
output_nonlinearity=None,
min_std=np.exp(-20.),
max_std=np.exp(2.),
)
qf1 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[1, 1],
hidden_nonlinearity=F.relu)
qf2 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[1, 1],
hidden_nonlinearity=F.relu)
replay_buffer = PathBuffer(capacity_in_transitions=int(1e6), )
num_tasks = 2
buffer_batch_size = 2
mtsac = MTSAC(policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=150,
max_episode_length=150,
eval_env=env,
env_spec=env.spec,
num_tasks=num_tasks,
steps_per_epoch=5,
replay_buffer=replay_buffer,
min_buffer_size=1e3,
target_update_tau=5e-3,
discount=0.99,
buffer_batch_size=buffer_batch_size)
monkeypatch.setattr(mtsac, '_log_alpha', torch.Tensor([1., 2.]))
for i, _ in enumerate(env_names):
obs = torch.Tensor([env.reset()[0]] * buffer_batch_size)
log_alpha = mtsac._get_log_alpha(dict(observation=obs))
assert (log_alpha == torch.Tensor([i + 1, i + 1])).all().item()
assert log_alpha.size() == torch.Size([mtsac._buffer_batch_size])
def test_get_action(self, obs_dim, action_dim, obs_type):
"""Test get_action method"""
assert obs_type in ['box', 'dict']
if obs_type == 'box':
env = GymEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))
else:
env = GymEnv(
DummyDictEnv(obs_space_type='box', act_space_type='box'))
policy = ContinuousMLPPolicy(env_spec=env.spec)
env.reset()
obs = env.step(1).observation
if obs_type == 'box':
obs = obs.flatten()
action, _ = policy.get_action(obs)
assert env.action_space.contains(action)
actions, _ = policy.get_actions([obs, obs, obs])
for action in actions:
assert env.action_space.contains(action)
def test_get_action(self, obs_dim, action_dim, hidden_dim):
env = GymEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))
policy = GaussianGRUPolicy(env_spec=env.spec,
hidden_dim=hidden_dim,
state_include_action=False)
policy.reset(do_resets=None)
obs = env.reset()[0]
action, _ = policy.get_action(obs.flatten())
assert env.action_space.contains(action)
actions, _ = policy.get_actions([obs.flatten()])
for action in actions:
assert env.action_space.contains(action)
def ppo_pendulum(ctxt=None, seed=1):
"""Train PPO with InvertedDoublePendulum-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.
"""
set_seed(seed)
with TFTrainer(snapshot_config=ctxt) as trainer:
env = normalize(GymEnv('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,
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,
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)
trainer.train(n_epochs=120, batch_size=2048, plot=False)
def test_all_gym_envs_pickleable(self, spec):
if spec._env_name.startswith('Defender'):
pytest.skip(
'Defender-* envs bundled in atari-py 0.2.x don\'t load')
if 'Kuka' in spec.id:
# Kuka environments calls py_bullet.resetSimulation() in reset()
# unconditionally, which globally resets other simulations. So
# only one Kuka environment can be tested.
pytest.skip('Skip Kuka Bullet environments')
elif 'Minitaur' in spec.id:
pytest.skip('Bulle Minitaur envs don\'t load')
env = GymEnv(spec.id)
round_trip = pickle.loads(pickle.dumps(env))
assert round_trip
def test_get_action(self, obs_dim, action_dim):
env = GymEnv(DummyBoxEnv(obs_dim=obs_dim, action_dim=action_dim))
policy = GaussianMLPPolicy(env_spec=env.spec)
env.reset()
obs = env.step(1).observation
action, _ = policy.get_action(obs.flatten())
assert env.action_space.contains(action)
actions, _ = policy.get_actions(
[obs.flatten(), obs.flatten(),
obs.flatten()])
for action in actions:
assert env.action_space.contains(action)
def ddpg_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow DDPG model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~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 = ContinuousMLPPolicy(
env_spec=env.spec,
hidden_sizes=hyper_parameters['policy_hidden_sizes'],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh)
exploration_policy = AddOrnsteinUhlenbeckNoise(
env.spec, policy, sigma=hyper_parameters['sigma'])
qf = ContinuousMLPQFunction(
env_spec=env.spec,
hidden_sizes=hyper_parameters['qf_hidden_sizes'],
hidden_nonlinearity=tf.nn.relu)
replay_buffer = PathBuffer(
capacity_in_transitions=hyper_parameters['replay_buffer_size'])
algo = DDPG(env_spec=env.spec,
policy=policy,
qf=qf,
replay_buffer=replay_buffer,
steps_per_epoch=hyper_parameters['steps_per_epoch'],
policy_lr=hyper_parameters['policy_lr'],
qf_lr=hyper_parameters['qf_lr'],
target_update_tau=hyper_parameters['tau'],
n_train_steps=hyper_parameters['n_train_steps'],
discount=hyper_parameters['discount'],
min_buffer_size=int(1e4),
exploration_policy=exploration_policy,
policy_optimizer=tf.compat.v1.train.AdamOptimizer,
qf_optimizer=tf.compat.v1.train.AdamOptimizer)
trainer.setup(algo, env)
trainer.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['n_exploration_steps'])
def _init_multi_env_wrapper(self,
env_names,
sample_strategy=uniform_random_strategy):
"""helper function to initialize multi_env_wrapper
Args:
env_names (list(str)): List of Environment names.
sample_strategy (func): A sampling strategy.
Returns:
garage.envs.multi_env_wrapper: Multi env wrapper.
"""
task_envs = [GymEnv(name) for name in env_names]
return MultiEnvWrapper(task_envs, sample_strategy=sample_strategy)
def test_get_action_dict_space(self):
env = GymEnv(DummyDictEnv(obs_space_type='box', act_space_type='box'))
policy = GaussianGRUPolicy(env_spec=env.spec,
hidden_dim=4,
state_include_action=False)
policy.reset(do_resets=None)
obs = env.reset()[0]
action, _ = policy.get_action(obs)
assert env.action_space.contains(action)
actions, _ = policy.get_actions([obs, obs])
for action in actions:
assert env.action_space.contains(action)
def test_all_gym_envs(self, spec):
if spec._env_name.startswith('Defender'):
pytest.skip(
'Defender-* envs bundled in atari-py 0.2.x don\'t load')
if spec._env_name.startswith('CarRacing'):
pytest.skip(
'CarRacing-* envs bundled in atari-py 0.2.x don\'t load')
if 'Kuka' in spec.id:
# Kuka environments calls py_bullet.resetSimulation() in reset()
# unconditionally, which globally resets other simulations. So
# only one Kuka environment can be tested.
pytest.skip('Skip Kuka Bullet environments')
env = GymEnv(spec.id)
step_env_with_gym_quirks(env, spec, visualize=False)
def ppo_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch PPO 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)
trainer = Trainer(ctxt)
env = normalize(GymEnv(env_id))
policy = PyTorch_GMP(env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
policy_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
policy,
max_optimization_epochs=10,
minibatch_size=64)
vf_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
value_function,
max_optimization_epochs=10,
minibatch_size=64)
algo = PyTorch_PPO(env_spec=env.spec,
policy=policy,
value_function=value_function,
policy_optimizer=policy_optimizer,
vf_optimizer=vf_optimizer,
discount=0.99,
gae_lambda=0.95,
center_adv=True,
lr_clip_range=0.2)
trainer.setup(algo, env)
trainer.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
def dqn_cartpole(ctxt=None, seed=1):
"""Train TRPO with CubeCrash-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(ctxt) as trainer:
n_epochs = 10
steps_per_epoch = 10
sampler_batch_size = 500
num_timesteps = n_epochs * steps_per_epoch * sampler_batch_size
env = GymEnv('CartPole-v0')
replay_buffer = PathBuffer(capacity_in_transitions=int(1e4))
qf = DiscreteMLPQFunction(env_spec=env.spec, hidden_sizes=(64, 64))
policy = DiscreteQFArgmaxPolicy(env_spec=env.spec, qf=qf)
exploration_policy = EpsilonGreedyPolicy(env_spec=env.spec,
policy=policy,
total_timesteps=num_timesteps,
max_epsilon=1.0,
min_epsilon=0.02,
decay_ratio=0.1)
sampler = LocalSampler(agents=exploration_policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
is_tf_worker=True,
worker_class=FragmentWorker)
algo = DQN(env_spec=env.spec,
policy=policy,
qf=qf,
exploration_policy=exploration_policy,
replay_buffer=replay_buffer,
sampler=sampler,
steps_per_epoch=steps_per_epoch,
qf_lr=1e-4,
discount=1.0,
min_buffer_size=int(1e3),
double_q=True,
n_train_steps=500,
target_network_update_freq=1,
buffer_batch_size=32)
trainer.setup(algo, env)
trainer.train(n_epochs=n_epochs, batch_size=sampler_batch_size)
def test_obs_not_image(self):
env = GymEnv(DummyDiscretePixelEnv())
with mock.patch(('tests.fixtures.models.SimpleCNNModel._build'),
autospec=True,
side_effect=SimpleCNNModel._build) as build:
with mock.patch(('garage.tf.models.'
'cnn_mlp_merge_model.CNNModel'),
new=SimpleCNNModel):
with mock.patch(('garage.tf.models.'
'cnn_mlp_merge_model.MLPMergeModel'),
new=SimpleMLPMergeModel):
qf = ContinuousCNNQFunction(env_spec=env.spec,
filters=((5, (3, 3)), ),
strides=(1, ))
# ensure non-image obses are not normalized
# in _initialize() and get_qval()
normalized_obs = build.call_args_list[0][0][1]
assert normalized_obs == qf.inputs[0]
fake_obs = [
np.full(env.spec.observation_space.shape, 255.)
]
assert (self.sess.run(normalized_obs,
feed_dict={qf.inputs[0]:
fake_obs}) == 255.).all()
# ensure non-image obses are not normalized in build()
obs_dim = env.spec.observation_space.shape
state_input = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
obs_dim)
act_dim = env.spec.observation_space.shape
action_input = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
act_dim)
qf.build(state_input, action_input, name='another')
normalized_obs = build.call_args_list[1][0][1]
assert (self.sess.run(normalized_obs,
feed_dict={state_input:
fake_obs}) == 255.).all()
