def test_to():
"""Test the torch function that moves modules to GPU.
Test that the policy and qfunctions are moved to gpu if gpu is
available.
"""
env_names = ['CartPole-v0', 'CartPole-v1']
task_envs = [GarageEnv(env_name=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_path_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)
tu.set_gpu_mode(torch.cuda.is_available())
mtsac.to()
device = tu.global_device()
for param in mtsac._qf1.parameters():
assert param.device == device
for param in mtsac._qf2.parameters():
assert param.device == device
for param in mtsac._qf2.parameters():
assert param.device == device
for param in mtsac._policy.parameters():
assert param.device == device
assert mtsac._log_alpha.device == device
def torch_sac_half_cheetah(ctxt=None, seed=1):
"""Set up environment and algorithm and run the task.
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.
"""
deterministic.set_seed(seed)
runner = LocalRunner(snapshot_config=ctxt)
env = GarageEnv(normalize(gym.make('HalfCheetah-v2')))
policy = TanhGaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=[256, 256],
hidden_nonlinearity=nn.ReLU,
output_nonlinearity=None,
min_std=np.exp(-20.),
max_std=np.exp(2.),
)
qf1 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[256, 256],
hidden_nonlinearity=F.relu)
qf2 = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[256, 256],
hidden_nonlinearity=F.relu)
replay_buffer = SimpleReplayBuffer(env_spec=env.spec,
size_in_transitions=int(1e6),
time_horizon=1)
sac = SAC(env_spec=env.spec,
policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=1000,
max_path_length=500,
use_automatic_entropy_tuning=True,
replay_buffer=replay_buffer,
min_buffer_size=1e4,
target_update_tau=5e-3,
discount=0.99,
buffer_batch_size=256,
reward_scale=1.,
steps_per_epoch=1)
if torch.cuda.is_available():
tu.set_gpu_mode(True)
else:
tu.set_gpu_mode(False)
sac.to()
runner.setup(algo=sac, env=env, sampler_cls=LocalSampler)
runner.train(n_epochs=1000, batch_size=1000)
def test_fixed_alpha():
"""Test if using fixed_alpha ensures that alpha is non differentiable."""
env_names = ['InvertedDoublePendulum-v2', 'InvertedDoublePendulum-v2']
task_envs = [GarageEnv(env_name=name) for name in env_names]
env = MultiEnvWrapper(task_envs, sample_strategy=round_robin_strategy)
test_envs = MultiEnvWrapper(task_envs,
sample_strategy=round_robin_strategy)
deterministic.set_seed(0)
runner = LocalRunner(snapshot_config=snapshot_config)
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), )
num_tasks = 2
buffer_batch_size = 128
mtsac = MTSAC(policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=100,
max_path_length=100,
eval_env=test_envs,
env_spec=env.spec,
num_tasks=num_tasks,
steps_per_epoch=1,
replay_buffer=replay_buffer,
min_buffer_size=1e3,
target_update_tau=5e-3,
discount=0.99,
buffer_batch_size=buffer_batch_size,
fixed_alpha=np.exp(0.5))
if torch.cuda.is_available():
tu.set_gpu_mode(True)
else:
tu.set_gpu_mode(False)
mtsac.to()
assert torch.allclose(torch.Tensor([0.5] * num_tasks),
mtsac._log_alpha.to('cpu'))
runner.setup(mtsac, env, sampler_cls=LocalSampler)
runner.train(n_epochs=1, batch_size=128, plot=False)
assert torch.allclose(torch.Tensor([0.5] * num_tasks),
mtsac._log_alpha.to('cpu'))
assert not mtsac._use_automatic_entropy_tuning
def test_utils_set_gpu_mode():
"""Test setting gpu mode to False to force CPU."""
if torch.cuda.is_available():
tu.set_gpu_mode(mode=True)
assert tu.global_device() == torch.device('cuda:0')
assert tu._USE_GPU
else:
tu.set_gpu_mode(mode=False)
assert tu.global_device() == torch.device('cpu')
assert not tu._USE_GPU
assert not tu._GPU_ID
def test_sac_inverted_double_pendulum():
"""Test Sac performance on inverted pendulum."""
# pylint: disable=unexpected-keyword-arg
env = GarageEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
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,
max_path_length=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():
tu.set_gpu_mode(True)
else:
tu.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 > 85
def test_sac_inverted_pendulum():
"""Test Sac performance on inverted pendulum."""
# pylint: disable=unexpected-keyword-arg
env = GarageEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
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 = SimpleReplayBuffer(env_spec=env.spec,
size_in_transitions=int(1e6),
time_horizon=1)
runner = LocalRunner(snapshot_config=snapshot_config)
sac = SAC(env_spec=env.spec,
policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=100,
max_path_length=100,
use_automatic_entropy_tuning=True,
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():
tu.set_gpu_mode(True)
else:
tu.set_gpu_mode(False)
sac.to()
ret = runner.train(n_epochs=12, batch_size=200, plot=False)
assert ret > 85
def diayn_half_cheetah_vel_batch_for_pearl(ctxt=None, seed=1):
deterministic.set_seed(seed)
runner = LocalRunner(snapshot_config=ctxt)
env = GarageEnv(normalize(HalfCheetahVelEnv()))
policy = TanhGaussianMLPSkillPolicy(
env_spec=env.spec,
skills_num=skills_num,
hidden_sizes=[256, 256],
hidden_nonlinearity=nn.ReLU,
output_nonlinearity=None,
min_std=np.exp(-20.),
max_std=np.exp(2.),
)
qf1 = ContinuousMLPSkillQFunction(env_spec=env.spec,
skills_num=skills_num,
hidden_sizes=[256, 256],
hidden_nonlinearity=F.relu)
qf2 = ContinuousMLPSkillQFunction(env_spec=env.spec,
skills_num=skills_num,
hidden_sizes=[256, 256],
hidden_nonlinearity=F.relu)
discriminator = MLPDiscriminator(env_spec=env.spec,
skills_num=skills_num,
hidden_sizes=[64, 64],
hidden_nonlinearity=F.relu)
replay_buffer = PathBuffer(capacity_in_transitions=int(1e6))
diayn = DIAYN(
env_spec=env.spec,
skills_num=skills_num,
discriminator=discriminator,
policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=1000,
max_path_length=300,
replay_buffer=replay_buffer,
min_buffer_size=1e4,
recorded=True, # enable the video recording func
target_update_tau=5e-3,
discount=0.99,
buffer_batch_size=256,
reward_scale=1.,
steps_per_epoch=1)
if torch.cuda.is_available():
tu.set_gpu_mode(True)
else:
tu.set_gpu_mode(False)
diayn.to()
worker_args = {"skills_num": skills_num}
runner.setup(algo=diayn,
env=env,
sampler_cls=LocalSkillSampler,
worker_class=SkillWorker,
worker_args=worker_args)
runner.train(n_epochs=1000, batch_size=1000) # 1000
# runner.restore(from_dir=os.path.join(os.getcwd(), 'data/local/experiment/diayn_half_cheetah_batch_50'))
# diayn = runner.get_algo()
runner.save(999) # saves the last episode
return discriminator, diayn
def pearl_half_cheetah(
ctxt=None,
seed=1,
num_epochs=param_num_epoches,
num_train_tasks=param_train_tasks_num,
num_test_tasks=param_test_tasks_num,
latent_size=param_latent_size,
encoder_hidden_size=param_encoder_hidden_size,
net_size=param_net_size,
meta_batch_size=param_meta_batch_size,
num_steps_per_epoch=param_num_steps_per_epoch,
num_initial_steps=param_num_initial_steps,
num_tasks_sample=param_num_tasks_sample,
num_steps_prior=param_num_steps_prior,
num_extra_rl_steps_posterior=param_num_extra_rl_steps_posterior,
batch_size=param_batch_size,
embedding_batch_size=param_embedding_batch_size,
embedding_mini_batch_size=param_embedding_mini_batch_size,
max_path_length=param_max_path_length,
reward_scale=param_reward_scale,
use_gpu=param_use_gpu):
set_seed(seed)
encoder_hidden_sizes = (encoder_hidden_size, encoder_hidden_size,
encoder_hidden_size)
# create multi-task environment and sample tasks
env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(HalfCheetahVelEnv())))
env = env_sampler.sample(num_train_tasks)
test_env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(HalfCheetahVelEnv())))
runner = LocalRunner(ctxt)
# instantiate networks
augmented_env = PEARL.augment_env_spec(env[0](), latent_size)
qf = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), latent_size, 'vf')
vf = ContinuousMLPQFunction(env_spec=vf_env,
hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(
env=env,
policy_class=ContextConditionedPolicy,
encoder_class=MLPEncoder,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=num_train_tasks,
num_test_tasks=num_test_tasks,
latent_dim=latent_size,
encoder_hidden_sizes=encoder_hidden_sizes,
test_env_sampler=test_env_sampler,
meta_batch_size=meta_batch_size,
num_steps_per_epoch=num_steps_per_epoch,
num_initial_steps=num_initial_steps,
num_tasks_sample=num_tasks_sample,
num_steps_prior=num_steps_prior,
num_extra_rl_steps_posterior=num_extra_rl_steps_posterior,
batch_size=batch_size,
embedding_batch_size=embedding_batch_size,
embedding_mini_batch_size=embedding_mini_batch_size,
max_path_length=max_path_length,
reward_scale=reward_scale,
)
tu.set_gpu_mode(use_gpu, gpu_id=0)
if use_gpu:
pearl.to()
runner.setup(algo=pearl,
env=env[0](),
sampler_cls=LocalSampler,
sampler_args=dict(max_path_length=max_path_length),
n_workers=1,
worker_class=PEARLWorker)
average_returns = runner.train(n_epochs=num_epochs, batch_size=batch_size)
runner.save(num_epochs - 1)
return average_returns
def diayn_pearl_half_cheeth(
ctxt=None,
seed=1,
num_epochs=param_num_epoches,
num_train_tasks=param_train_tasks_num,
num_test_tasks=param_test_tasks_num,
latent_size=param_latent_size,
encoder_hidden_size=param_encoder_hidden_size,
net_size=param_net_size,
meta_batch_size=param_meta_batch_size,
num_steps_per_epoch=param_num_steps_per_epoch,
num_initial_steps=param_num_initial_steps,
num_tasks_sample=param_num_tasks_sample,
num_steps_prior=param_num_steps_prior,
num_extra_rl_steps_posterior=param_num_extra_rl_steps_posterior,
batch_size=param_batch_size,
embedding_batch_size=param_embedding_batch_size,
embedding_mini_batch_size=param_embedding_mini_batch_size,
max_path_length=param_max_path_length,
reward_scale=param_reward_scale,
use_gpu=param_use_gpu):
if task_proposer is None:
raise ValueError("Task proposer is empty")
assert num_train_tasks is skills_num
set_seed(seed)
encoder_hidden_sizes = (encoder_hidden_size, encoder_hidden_size,
encoder_hidden_size)
# create multi-task environment and sample tasks
ML_train_envs = [
DiaynEnvWrapper(task_proposer, skills_num, task_name,
normalize(HalfCheetahVelEnv()))
for task_name in range(skills_num)
]
env_sampler = EnvPoolSampler(ML_train_envs)
env = env_sampler.sample(num_train_tasks)
# train_trajs_dist = [train_env.get_training_traj(diayn_trained_agent)
# for train_env in ML_train_envs]
# ML_test_envs = [
# GarageEnv(normalize(
# DiaynEnvWrapper(env, task_proposer, skills_num, task_name)))
# for task_name in random.sample(range(skills_num), test_tasks_num)
# ]
test_env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(HalfCheetahVelEnv())))
runner = LocalRunner(ctxt)
# instantiate networks
augmented_env = PEARL.augment_env_spec(env[0](), latent_size)
qf = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), latent_size, 'vf')
vf = ContinuousMLPQFunction(env_spec=vf_env,
hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(
env=env,
policy_class=ContextConditionedPolicy,
encoder_class=MLPEncoder,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=num_train_tasks,
num_test_tasks=num_test_tasks,
latent_dim=latent_size,
encoder_hidden_sizes=encoder_hidden_sizes,
test_env_sampler=test_env_sampler,
meta_batch_size=meta_batch_size,
num_steps_per_epoch=num_steps_per_epoch,
num_initial_steps=num_initial_steps,
num_tasks_sample=num_tasks_sample,
num_steps_prior=num_steps_prior,
num_extra_rl_steps_posterior=num_extra_rl_steps_posterior,
batch_size=batch_size,
embedding_batch_size=embedding_batch_size,
embedding_mini_batch_size=embedding_mini_batch_size,
max_path_length=max_path_length,
reward_scale=reward_scale,
)
tu.set_gpu_mode(use_gpu, gpu_id=0)
if use_gpu:
pearl.to()
runner.setup(algo=pearl,
env=env[0](),
sampler_cls=LocalSampler,
sampler_args=dict(max_path_length=max_path_length),
n_workers=1,
worker_class=PEARLWorker)
average_returns = runner.train(n_epochs=num_epochs, batch_size=batch_size)
runner.save(num_epochs - 1)
return average_returns
def mtsac_metaworld_ml1_pick_place(ctxt=None, seed=1, _gpu=None):
"""Train MTSAC with the ML1 pick-place-v1 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.
_gpu (int): The ID of the gpu to be used (used on multi-gpu machines).
"""
deterministic.set_seed(seed)
runner = LocalRunner(ctxt)
train_envs = []
test_envs = []
env_names = []
for i in range(50):
train_env = GarageEnv(
normalize(mwb.ML1.get_train_tasks('pick-place-v1'),
normalize_reward=True))
test_env = pickle.loads(pickle.dumps(train_env))
env_names.append('pick_place_{}'.format(i))
train_envs.append(train_env)
test_envs.append(test_env)
ml1_train_envs = MultiEnvWrapper(train_envs,
sample_strategy=round_robin_strategy,
env_names=env_names)
ml1_test_envs = MultiEnvWrapper(test_envs,
sample_strategy=round_robin_strategy,
env_names=env_names)
policy = TanhGaussianMLPPolicy(
env_spec=ml1_train_envs.spec,
hidden_sizes=[400, 400, 400],
hidden_nonlinearity=nn.ReLU,
output_nonlinearity=None,
min_std=np.exp(-20.),
max_std=np.exp(2.),
)
qf1 = ContinuousMLPQFunction(env_spec=ml1_train_envs.spec,
hidden_sizes=[400, 400, 400],
hidden_nonlinearity=F.relu)
qf2 = ContinuousMLPQFunction(env_spec=ml1_train_envs.spec,
hidden_sizes=[400, 400, 400],
hidden_nonlinearity=F.relu)
replay_buffer = PathBuffer(capacity_in_transitions=int(1e6), )
timesteps = 10000000
batch_size = int(150 * ml1_train_envs.num_tasks)
num_evaluation_points = 500
epochs = timesteps // batch_size
epoch_cycles = epochs // num_evaluation_points
epochs = epochs // epoch_cycles
mtsac = MTSAC(policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=150,
max_path_length=150,
eval_env=ml1_test_envs,
env_spec=ml1_train_envs.spec,
num_tasks=50,
steps_per_epoch=epoch_cycles,
replay_buffer=replay_buffer,
min_buffer_size=1500,
target_update_tau=5e-3,
discount=0.99,
buffer_batch_size=1280)
if _gpu is not None:
tu.set_gpu_mode(True, _gpu)
mtsac.to()
runner.setup(algo=mtsac, env=ml1_train_envs, sampler_cls=LocalSampler)
runner.train(n_epochs=epochs, batch_size=batch_size)
def pearl_metaworld_ml45(ctxt=None,
seed=1,
num_epochs=1000,
num_train_tasks=45,
num_test_tasks=5,
latent_size=7,
encoder_hidden_size=200,
net_size=300,
meta_batch_size=16,
num_steps_per_epoch=4000,
num_initial_steps=4000,
num_tasks_sample=15,
num_steps_prior=750,
num_extra_rl_steps_posterior=750,
batch_size=256,
embedding_batch_size=64,
embedding_mini_batch_size=64,
max_path_length=150,
reward_scale=10.,
use_gpu=False):
"""Train PEARL with ML45 environments.
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.
num_epochs (int): Number of training epochs.
num_train_tasks (int): Number of tasks for training.
num_test_tasks (int): Number of tasks for testing.
latent_size (int): Size of latent context vector.
encoder_hidden_size (int): Output dimension of dense layer of the
context encoder.
net_size (int): Output dimension of a dense layer of Q-function and
value function.
meta_batch_size (int): Meta batch size.
num_steps_per_epoch (int): Number of iterations per epoch.
num_initial_steps (int): Number of transitions obtained per task before
training.
num_tasks_sample (int): Number of random tasks to obtain data for each
iteration.
num_steps_prior (int): Number of transitions to obtain per task with
z ~ prior.
num_extra_rl_steps_posterior (int): Number of additional transitions
to obtain per task with z ~ posterior that are only used to train
the policy and NOT the encoder.
batch_size (int): Number of transitions in RL batch.
embedding_batch_size (int): Number of transitions in context batch.
embedding_mini_batch_size (int): Number of transitions in mini context
batch; should be same as embedding_batch_size for non-recurrent
encoder.
max_path_length (int): Maximum path length.
reward_scale (int): Reward scale.
use_gpu (bool): Whether or not to use GPU for training.
"""
set_seed(seed)
encoder_hidden_sizes = (encoder_hidden_size, encoder_hidden_size,
encoder_hidden_size)
# create multi-task environment and sample tasks
ml45_train_envs = [
GarageEnv(normalize(mwb.ML45.from_task(task_name)))
for task_name in mwb.ML45.get_train_tasks().all_task_names
]
ml45_test_envs = [
GarageEnv(normalize(mwb.ML45.from_task(task_name)))
for task_name in mwb.ML45.get_test_tasks().all_task_names
]
env_sampler = EnvPoolSampler(ml45_train_envs)
env_sampler.grow_pool(num_train_tasks)
env = env_sampler.sample(num_train_tasks)
test_env_sampler = EnvPoolSampler(ml45_test_envs)
test_env_sampler.grow_pool(num_test_tasks)
runner = LocalRunner(ctxt)
# instantiate networks
augmented_env = PEARL.augment_env_spec(env[0](), latent_size)
qf = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), latent_size, 'vf')
vf = ContinuousMLPQFunction(env_spec=vf_env,
hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(
env=env,
policy_class=ContextConditionedPolicy,
encoder_class=MLPEncoder,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=num_train_tasks,
num_test_tasks=num_test_tasks,
latent_dim=latent_size,
encoder_hidden_sizes=encoder_hidden_sizes,
test_env_sampler=test_env_sampler,
meta_batch_size=meta_batch_size,
num_steps_per_epoch=num_steps_per_epoch,
num_initial_steps=num_initial_steps,
num_tasks_sample=num_tasks_sample,
num_steps_prior=num_steps_prior,
num_extra_rl_steps_posterior=num_extra_rl_steps_posterior,
batch_size=batch_size,
embedding_batch_size=embedding_batch_size,
embedding_mini_batch_size=embedding_mini_batch_size,
max_path_length=max_path_length,
reward_scale=reward_scale,
)
tu.set_gpu_mode(use_gpu, gpu_id=0)
if use_gpu:
pearl.to()
runner.setup(algo=pearl,
env=env[0](),
sampler_cls=LocalSampler,
sampler_args=dict(max_path_length=max_path_length),
n_workers=1,
worker_class=PEARLWorker)
runner.train(n_epochs=num_epochs, batch_size=batch_size)
def meta_kant_cheetah_vel(
ctxt=None,
seed=seed,
num_skills=skills_num,
num_epochs=param_num_epoches,
num_train_tasks=param_train_tasks_num,
num_test_tasks=param_test_tasks_num,
is_encoder_recurrent=False,
latent_size=param_latent_size,
encoder_hidden_size=param_encoder_hidden_size,
net_size=param_net_size,
meta_batch_size=param_meta_batch_size,
num_steps_per_epoch=param_num_steps_per_epoch,
num_initial_steps=param_num_initial_steps,
num_tasks_sample=param_num_tasks_sample,
num_steps_prior=param_num_steps_prior,
num_extra_rl_steps_posterior=param_num_extra_rl_steps_posterior,
num_skills_sample=param_num_skills_sample,
num_skills_reason_steps=param_num_skills_reason_steps,
batch_size=param_batch_size,
embedding_batch_size=param_embedding_batch_size,
embedding_mini_batch_size=param_embedding_mini_batch_size,
max_path_length=param_max_path_length,
skills_reason_reward_scale=param_skills_reason_reward_scale,
tasks_adapt_reward_scale=param_tasks_adapt_reward_scale,
use_gpu=param_use_gpu):
assert num_train_tasks is skills_num
set_seed(seed)
encoder_hidden_sizes = (encoder_hidden_size, encoder_hidden_size,
encoder_hidden_size)
ML_train_envs = [
DiaynEnvWrapper(task_proposer, skills_num, task_name,
normalize(HalfCheetahVelEnv()))
for task_name in range(skills_num)
]
env_sampler = EnvPoolSampler(ML_train_envs)
env = env_sampler.sample(num_train_tasks)
test_env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(HalfCheetahVelEnv())))
runner = LocalRunner(ctxt)
qf_env = MetaKant.get_env_spec(env[0](), latent_size, num_skills, "qf")
qf = ContinuousMLPQFunction(env_spec=qf_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = MetaKant.get_env_spec(env[0](), latent_size, num_skills, 'vf')
vf = ContinuousMLPQFunction(env_spec=vf_env,
hidden_sizes=[net_size, net_size, net_size])
controller_policy_env = MetaKant.get_env_spec(env[0](),
latent_size,
module="controller_policy",
num_skills=num_skills)
controller_policy = CategoricalMLPPolicy(
env_spec=controller_policy_env,
hidden_sizes=[net_size, net_size],
hidden_nonlinearity=functional.relu)
metakant = MetaKant(
env=env,
skill_env=skill_env,
controller_policy=controller_policy,
skill_actor=skill_actor,
qf=qf,
vf=vf,
num_skills=num_skills,
num_train_tasks=num_train_tasks,
num_test_tasks=num_test_tasks,
sampler_class=LocalSkillSampler,
is_encoder_recurrent=is_encoder_recurrent,
latent_dim=latent_size,
encoder_hidden_sizes=encoder_hidden_sizes,
test_env_sampler=test_env_sampler,
meta_batch_size=meta_batch_size,
num_initial_steps=num_initial_steps,
num_tasks_sample=num_tasks_sample,
num_steps_per_epoch=num_steps_per_epoch,
num_steps_prior=num_steps_prior, # num_steps_posterior
num_extra_rl_steps_posterior=num_extra_rl_steps_posterior,
num_skills_reason_steps=num_skills_reason_steps,
num_skills_sample=num_skills_sample,
batch_size=batch_size,
embedding_batch_size=embedding_batch_size,
embedding_mini_batch_size=embedding_mini_batch_size,
max_path_length=max_path_length,
skills_reason_reward_scale=skills_reason_reward_scale,
tasks_adapt_reward_scale=tasks_adapt_reward_scale)
tu.set_gpu_mode(use_gpu, gpu_id=0)
if use_gpu:
metakant.to()
worker_args = dict(num_skills=num_skills,
skill_actor_class=type(skill_actor),
controller_class=OpenContextConditionedControllerPolicy,
deterministic=False,
accum_context=True)
runner.setup(algo=metakant,
env=env[0](),
sampler_cls=LocalSkillSampler,
sampler_args=dict(max_path_length=max_path_length),
n_workers=1,
worker_class=KantWorker,
worker_args=worker_args)
average_returns = runner.train(n_epochs=num_epochs, batch_size=batch_size)
runner.save(num_epochs - 1)
return average_returns
def mtsac_metaworld_mt50(ctxt=None, seed=1, use_gpu=False, _gpu=0):
"""Train MTSAC with MT50 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.
use_gpu (bool): Used to enable ussage of GPU in training.
_gpu (int): The ID of the gpu (used on multi-gpu machines).
"""
deterministic.set_seed(seed)
runner = LocalRunner(ctxt)
task_names = mwb.MT50.get_train_tasks().all_task_names
train_envs = []
test_envs = []
for task_name in task_names:
train_env = normalize(GarageEnv(mwb.MT50.from_task(task_name)),
normalize_reward=True)
test_env = normalize(GarageEnv(mwb.MT50.from_task(task_name)))
train_envs.append(train_env)
test_envs.append(test_env)
mt50_train_envs = MultiEnvWrapper(train_envs,
sample_strategy=round_robin_strategy,
mode='vanilla')
mt50_test_envs = MultiEnvWrapper(test_envs,
sample_strategy=round_robin_strategy,
mode='vanilla')
policy = TanhGaussianMLPPolicy(
env_spec=mt50_train_envs.spec,
hidden_sizes=[400, 400, 400],
hidden_nonlinearity=nn.ReLU,
output_nonlinearity=None,
min_std=np.exp(-20.),
max_std=np.exp(2.),
)
qf1 = ContinuousMLPQFunction(env_spec=mt50_train_envs.spec,
hidden_sizes=[400, 400, 400],
hidden_nonlinearity=F.relu)
qf2 = ContinuousMLPQFunction(env_spec=mt50_train_envs.spec,
hidden_sizes=[400, 400, 400],
hidden_nonlinearity=F.relu)
replay_buffer = PathBuffer(capacity_in_transitions=int(1e6), )
timesteps = 100000000
batch_size = int(150 * mt50_train_envs.num_tasks)
num_evaluation_points = 500
epochs = timesteps // batch_size
epoch_cycles = epochs // num_evaluation_points
epochs = epochs // epoch_cycles
mtsac = MTSAC(policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=150,
max_path_length=250,
eval_env=mt50_test_envs,
env_spec=mt50_train_envs.spec,
num_tasks=10,
steps_per_epoch=epoch_cycles,
replay_buffer=replay_buffer,
min_buffer_size=7500,
target_update_tau=5e-3,
discount=0.99,
buffer_batch_size=6400)
tu.set_gpu_mode(use_gpu, _gpu)
mtsac.to()
runner.setup(algo=mtsac, env=mt50_train_envs, sampler_cls=LocalSampler)
runner.train(n_epochs=epochs, batch_size=batch_size)
def test_pearl_ml1_push(self):
"""Test PEARL with ML1 Push environment."""
params = dict(seed=1,
num_epochs=1,
num_train_tasks=5,
num_test_tasks=1,
latent_size=7,
encoder_hidden_sizes=[10, 10, 10],
net_size=30,
meta_batch_size=16,
num_steps_per_epoch=40,
num_initial_steps=40,
num_tasks_sample=15,
num_steps_prior=15,
num_extra_rl_steps_posterior=15,
batch_size=256,
embedding_batch_size=8,
embedding_mini_batch_size=8,
max_path_length=50,
reward_scale=10.,
use_information_bottleneck=True,
use_next_obs_in_context=False,
use_gpu=False)
net_size = params['net_size']
set_seed(params['seed'])
env_sampler = SetTaskSampler(lambda: GarageEnv(
normalize(ML1.get_train_tasks('push-v1'))))
env = env_sampler.sample(params['num_train_tasks'])
test_env_sampler = SetTaskSampler(lambda: GarageEnv(
normalize(ML1.get_test_tasks('push-v1'))))
augmented_env = PEARL.augment_env_spec(env[0](), params['latent_size'])
qf = ContinuousMLPQFunction(
env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), params['latent_size'], 'vf')
vf = ContinuousMLPQFunction(
env_spec=vf_env, hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(
env=env,
policy_class=ContextConditionedPolicy,
encoder_class=MLPEncoder,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=params['num_train_tasks'],
num_test_tasks=params['num_test_tasks'],
latent_dim=params['latent_size'],
encoder_hidden_sizes=params['encoder_hidden_sizes'],
test_env_sampler=test_env_sampler,
meta_batch_size=params['meta_batch_size'],
num_steps_per_epoch=params['num_steps_per_epoch'],
num_initial_steps=params['num_initial_steps'],
num_tasks_sample=params['num_tasks_sample'],
num_steps_prior=params['num_steps_prior'],
num_extra_rl_steps_posterior=params[
'num_extra_rl_steps_posterior'],
batch_size=params['batch_size'],
embedding_batch_size=params['embedding_batch_size'],
embedding_mini_batch_size=params['embedding_mini_batch_size'],
max_path_length=params['max_path_length'],
reward_scale=params['reward_scale'],
)
tu.set_gpu_mode(params['use_gpu'], gpu_id=0)
if params['use_gpu']:
pearl.to()
runner = LocalRunner(snapshot_config)
runner.setup(
algo=pearl,
env=env[0](),
sampler_cls=LocalSampler,
sampler_args=dict(max_path_length=params['max_path_length']),
n_workers=1,
worker_class=PEARLWorker)
runner.train(n_epochs=params['num_epochs'],
batch_size=params['batch_size'])
def diayn_point_mass_multigoal(ctxt=None, seed=1):
deterministic.set_seed(seed)
runner = LocalRunner(snapshot_config=ctxt)
env = MultiGoalEnv()
skills_num = 6
policy = TanhGaussianMLPSkillPolicy(
env_spec=env.spec,
skills_num=skills_num,
hidden_sizes=[256, 256],
hidden_nonlinearity=nn.ReLU,
output_nonlinearity=None,
min_std=np.exp(-20.),
max_std=np.exp(2.),
)
qf1 = ContinuousMLPSkillQFunction(env_spec=env.spec,
skills_num=skills_num,
hidden_sizes=[256, 256],
hidden_nonlinearity=F.relu)
qf2 = ContinuousMLPSkillQFunction(env_spec=env.spec,
skills_num=skills_num,
hidden_sizes=[256, 256],
hidden_nonlinearity=F.relu)
discriminator = MLPDiscriminator(env_spec=env.spec,
skills_num=skills_num,
hidden_sizes=[64, 64],
hidden_nonlinearity=F.relu)
replay_buffer = PathBuffer(capacity_in_transitions=int(1e6))
diayn = DIAYN(
env_spec=env.spec,
skills_num=skills_num,
discriminator=discriminator,
policy=policy,
qf1=qf1,
qf2=qf2,
gradient_steps_per_itr=1000,
max_path_length=500,
replay_buffer=replay_buffer,
min_buffer_size=1e4,
recorded=True, # enable the video recording func
is_gym_render=False,
media_save_path='diayn_2d_multigoal/',
target_update_tau=5e-3,
discount=0.99,
buffer_batch_size=256,
reward_scale=1.,
steps_per_epoch=1)
if torch.cuda.is_available():
tu.set_gpu_mode(True)
else:
tu.set_gpu_mode(False)
diayn.to()
worker_args = {"skills_num": skills_num}
runner.setup(algo=diayn,
env=env,
sampler_cls=LocalSkillSampler,
worker_class=SkillWorker,
worker_args=worker_args)
runner.train(n_epochs=1000, batch_size=1000)
