def vpg_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)
env = MetaRLEnv(env_name='InvertedDoublePendulum-v2')
runner = LocalRunner(ctxt)
policy = GaussianMLPPolicy(env.spec,
hidden_sizes=[64, 64],
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)
algo = VPG(env_spec=env.spec,
policy=policy,
value_function=value_function,
max_path_length=100,
discount=0.99,
center_adv=False)
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=10000)
def run_task(snapshot_config, *_):
"""Set up environment and algorithm and run the task.
Args:
snapshot_config (metarl.experiment.SnapshotConfig): The snapshot
configuration used by LocalRunner to create the snapshotter.
If None, it will create one with default settings.
_ : Unused parameters
"""
env = TfEnv(env_name='InvertedDoublePendulum-v2')
runner = LocalRunner(snapshot_config)
policy = GaussianMLPPolicy(env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = VPG(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
center_adv=False)
runner.setup(algo, env)
runner.train(n_epochs=100, batch_size=10000)
def test_vpg_regularized(self):
"""Test VPG with entropy_regularized."""
self._params['entropy_method'] = 'regularized'
algo = VPG(**self._params)
self._runner.setup(algo, self._env)
last_avg_ret = self._runner.train(n_epochs=10, batch_size=100)
assert last_avg_ret > 0
def test_vpg_no_entropy(self):
"""Test VPG with no_entropy."""
self._params['positive_adv'] = True
self._params['use_softplus_entropy'] = True
algo = VPG(**self._params)
self._runner.setup(algo, self._env)
last_avg_ret = self._runner.train(n_epochs=10, batch_size=100)
assert last_avg_ret > 0
def test_vpg_max(self):
"""Test VPG with maximum entropy."""
self._params['center_adv'] = False
self._params['stop_entropy_gradient'] = True
self._params['entropy_method'] = 'max'
algo = VPG(**self._params)
self._runner.setup(algo, self._env)
last_avg_ret = self._runner.train(n_epochs=10, batch_size=100)
assert last_avg_ret > 0
def __init__(self,
env,
policy,
value_function,
inner_lr=_Default(1e-2),
outer_lr=1e-3,
max_kl_step=0.01,
max_path_length=500,
discount=0.99,
gae_lambda=1,
center_adv=True,
positive_adv=False,
policy_ent_coeff=0.0,
use_softplus_entropy=False,
stop_entropy_gradient=False,
entropy_method='no_entropy',
meta_batch_size=40,
num_grad_updates=1,
meta_evaluator=None,
evaluate_every_n_epochs=1):
policy_optimizer = OptimizerWrapper(
(torch.optim.Adam, dict(lr=inner_lr)), policy)
vf_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=inner_lr)),
value_function)
inner_algo = VPG(env.spec,
policy,
value_function,
policy_optimizer=policy_optimizer,
vf_optimizer=vf_optimizer,
max_path_length=max_path_length,
num_train_per_epoch=1,
discount=discount,
gae_lambda=gae_lambda,
center_adv=center_adv,
positive_adv=positive_adv,
policy_ent_coeff=policy_ent_coeff,
use_softplus_entropy=use_softplus_entropy,
stop_entropy_gradient=stop_entropy_gradient,
entropy_method=entropy_method)
meta_optimizer = (ConjugateGradientOptimizer,
dict(max_constraint_value=max_kl_step))
super().__init__(inner_algo=inner_algo,
env=env,
policy=policy,
meta_optimizer=meta_optimizer,
meta_batch_size=meta_batch_size,
inner_lr=inner_lr,
outer_lr=outer_lr,
num_grad_updates=num_grad_updates,
meta_evaluator=meta_evaluator,
evaluate_every_n_epochs=evaluate_every_n_epochs)
def __init__(self,
env,
policy,
baseline,
inner_lr=_Default(1e-1),
outer_lr=1e-3,
max_path_length=100,
discount=0.99,
gae_lambda=1,
center_adv=True,
positive_adv=False,
policy_ent_coeff=0.0,
use_softplus_entropy=False,
stop_entropy_gradient=False,
entropy_method='no_entropy',
meta_batch_size=20,
num_grad_updates=1):
inner_algo = VPG(env.spec,
policy,
baseline,
optimizer=torch.optim.Adam,
policy_lr=inner_lr,
max_path_length=max_path_length,
num_train_per_epoch=1,
discount=discount,
gae_lambda=gae_lambda,
center_adv=center_adv,
positive_adv=positive_adv,
policy_ent_coeff=policy_ent_coeff,
use_softplus_entropy=use_softplus_entropy,
stop_entropy_gradient=stop_entropy_gradient,
entropy_method=entropy_method)
super().__init__(inner_algo=inner_algo,
env=env,
policy=policy,
baseline=baseline,
meta_optimizer=torch.optim.Adam,
meta_batch_size=meta_batch_size,
inner_lr=inner_lr,
outer_lr=outer_lr,
num_grad_updates=num_grad_updates)
def test_invalid_entropy_config(self, algo_param, error, msg):
"""Test VPG with invalid entropy config."""
self._params.update(algo_param)
with pytest.raises(error, match=msg):
VPG(**self._params)