def create_optimizer_mock(trainer_config, reward_signal_config, use_rnn,
use_discrete, use_visual):
mock_specs = mb.setup_test_behavior_specs(
use_discrete,
use_visual,
vector_action_space=DISCRETE_ACTION_SPACE
if use_discrete else VECTOR_ACTION_SPACE,
vector_obs_space=VECTOR_OBS_SPACE if not use_visual else 0,
)
trainer_settings = trainer_config
trainer_settings.reward_signals = reward_signal_config
trainer_settings.network_settings.memory = (NetworkSettings.MemorySettings(
sequence_length=16, memory_size=10) if use_rnn else None)
policy = TFPolicy(0,
mock_specs,
trainer_settings,
"test",
False,
create_tf_graph=False)
if trainer_settings.trainer_type == TrainerType.SAC:
optimizer = SACOptimizer(policy, trainer_settings)
else:
optimizer = PPOOptimizer(policy, trainer_settings)
optimizer.policy.initialize()
return optimizer
def create_ppo_optimizer(self) -> PPOOptimizer:
if self.framework == FrameworkType.PYTORCH:
return TorchPPOOptimizer( # type: ignore
cast(TorchPolicy, self.policy), self.trainer_settings # type: ignore
) # type: ignore
else:
return PPOOptimizer( # type: ignore
cast(TFPolicy, self.policy), self.trainer_settings # type: ignore
) # type: ignore
def _create_ppo_optimizer_ops_mock(dummy_config, use_rnn, use_discrete, use_visual):
mock_specs = mb.setup_test_behavior_specs(
use_discrete,
use_visual,
vector_action_space=DISCRETE_ACTION_SPACE
if use_discrete
else VECTOR_ACTION_SPACE,
vector_obs_space=VECTOR_OBS_SPACE,
)
trainer_settings = attr.evolve(dummy_config)
trainer_settings.network_settings.memory = (
NetworkSettings.MemorySettings(sequence_length=16, memory_size=10)
if use_rnn
else None
)
policy = TFPolicy(
0, mock_specs, trainer_settings, "test", False, create_tf_graph=False
)
optimizer = PPOOptimizer(policy, trainer_settings)
policy.initialize()
return optimizer