def sac_half_cheetah_batch(ctxt=None, seed=1):
"""Set up environment and algorithm and run the task.
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.
"""
deterministic.set_seed(seed)
trainer = Trainer(snapshot_config=ctxt)
env = normalize(GymEnv('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 = PathBuffer(capacity_in_transitions=int(1e6))
sampler = LocalSampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
worker_class=FragmentWorker)
sac = SAC(env_spec=env.spec,
policy=policy,
qf1=qf1,
qf2=qf2,
sampler=sampler,
gradient_steps_per_itr=1000,
max_episode_length_eval=1000,
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():
set_gpu_mode(True)
else:
set_gpu_mode(False)
sac.to()
trainer.setup(algo=sac, env=env)
trainer.train(n_epochs=1000, batch_size=1000)
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_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), )
trainer = Trainer(snapshot_config=snapshot_config)
sampler = LocalSampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
worker_class=FragmentWorker)
sac = SAC(env_spec=env.spec,
policy=policy,
qf1=qf1,
qf2=qf2,
sampler=sampler,
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)
trainer.setup(sac, env)
if torch.cuda.is_available():
set_gpu_mode(True)
else:
set_gpu_mode(False)
sac.to()
ret = trainer.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 test_sac_to():
"""Test moving Sac between CPU and GPU."""
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), )
trainer = Trainer(snapshot_config=snapshot_config)
sampler = LocalSampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
worker_class=FragmentWorker)
sac = SAC(env_spec=env.spec,
policy=policy,
qf1=qf1,
qf2=qf2,
sampler=sampler,
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)
trainer.setup(sac, env)
if torch.cuda.is_available():
set_gpu_mode(True)
else:
set_gpu_mode(False)
sac.to()
trainer.setup(algo=sac, env=env)
trainer.train(n_epochs=1, batch_size=100)
log_alpha = torch.clone(sac._log_alpha).cpu()
set_gpu_mode(False)
sac.to()
assert torch.allclose(log_alpha, sac._log_alpha)
def test_fixed_alpha():
"""Test if using fixed_alpha ensures that alpha is non differentiable."""
# 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), )
trainer = Trainer(snapshot_config=snapshot_config)
sampler = LocalSampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length,
worker_class=FragmentWorker)
sac = SAC(env_spec=env.spec,
policy=policy,
qf1=qf1,
qf2=qf2,
sampler=sampler,
gradient_steps_per_itr=100,
replay_buffer=replay_buffer,
min_buffer_size=100,
target_update_tau=5e-3,
discount=0.99,
buffer_batch_size=64,
reward_scale=1.,
steps_per_epoch=1,
fixed_alpha=np.exp(0.5))
trainer.setup(sac, env)
sac.to()
trainer.train(n_epochs=1, batch_size=100, plot=False)
assert torch.allclose(torch.Tensor([0.5]), sac._log_alpha.cpu())
assert not sac._use_automatic_entropy_tuning
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
