def test_actor_wrapper(self):
state_normalization_parameters = {i: _cont_norm() for i in range(1, 5)}
action_normalization_parameters = {
i: _cont_action_norm() for i in range(101, 105)
}
state_preprocessor = Preprocessor(state_normalization_parameters, False)
postprocessor = Postprocessor(action_normalization_parameters, False)
# Test with FullyConnectedActor to make behavior deterministic
actor = FullyConnectedActor(
state_dim=len(state_normalization_parameters),
action_dim=len(action_normalization_parameters),
sizes=[16],
activations=["relu"],
)
actor_with_preprocessor = ActorWithPreprocessor(
actor, state_preprocessor, postprocessor
)
wrapper = ActorPredictorWrapper(actor_with_preprocessor)
input_prototype = actor_with_preprocessor.input_prototype()
action = wrapper(*input_prototype)
self.assertEqual(action.shape, (1, len(action_normalization_parameters)))
expected_output = postprocessor(
actor(
rlt.PreprocessedState.from_tensor(
state_preprocessor(*input_prototype[0])
)
).action
)
self.assertTrue((expected_output == action).all())
def get_actor_predictor(self, trainer, environment):
state_preprocessor = Preprocessor(environment.normalization, False)
postprocessor = Postprocessor(
environment.normalization_continuous_action, False)
actor_with_preprocessor = ActorWithPreprocessor(
trainer.actor_network.cpu_model().eval(), state_preprocessor,
postprocessor)
serving_module = ActorPredictorWrapper(actor_with_preprocessor)
predictor = ActorTorchPredictor(
serving_module,
sort_features_by_normalization(
environment.normalization_continuous_action)[0],
)
return predictor