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_test_ppo_optimizer(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 = TorchPolicy(0, mock_specs, trainer_settings, "test", False)
optimizer = TorchPPOOptimizer(policy, trainer_settings)
return optimizer
def create_ppo_optimizer(self) -> TorchPPOOptimizer:
return TorchPPOOptimizer( # type: ignore
cast(TorchPolicy, self.policy), self.trainer_settings # type: ignore
) # type: ignore