def create_sac_optimizer(self) -> SACOptimizer:
if self.framework == FrameworkType.PYTORCH:
return TorchSACOptimizer( # type: ignore
cast(TorchPolicy, self.policy),
self.trainer_settings # type: ignore
) # type: ignore
else:
return SACOptimizer( # type: ignore
cast(TFPolicy, self.policy),
self.trainer_settings # type: ignore
) # type: ignore
def create_sac_optimizer_mock(dummy_config, use_rnn, use_discrete, use_visual):
mock_brain = 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 = dummy_config
trainer_settings.network_settings.memory = (NetworkSettings.MemorySettings(
sequence_length=16, memory_size=12) if use_rnn else None)
policy = TorchPolicy(0, mock_brain, trainer_settings)
optimizer = TorchSACOptimizer(policy, trainer_settings)
return optimizer
def create_sac_optimizer(self) -> TorchSACOptimizer:
return TorchSACOptimizer( # type: ignore
cast(TorchPolicy, self.policy),
self.trainer_settings # type: ignore
) # type: ignore
