def test_create_net_max_q_discrete_action(self):
extractor = TrainingFeatureExtractor(
state_normalization_parameters=self.get_state_normalization_parameters(),
include_possible_actions=True,
max_num_actions=2,
)
expected_input_record = schema.Struct(
("state_features", map_schema()),
("next_state_features", map_schema()),
("action", schema.Scalar()),
("next_action", schema.Scalar()),
("not_terminal", schema.Scalar()),
("possible_actions_mask", schema.List(schema.Scalar())),
("possible_next_actions_mask", schema.List(schema.Scalar())),
)
expected_output_record = schema.Struct(
("state_features", schema.Scalar()),
("next_state_features", schema.Scalar()),
("action", schema.Scalar()),
("next_action", schema.Scalar()),
("not_terminal", schema.Scalar()),
("possible_actions_mask", schema.Scalar()),
("possible_next_actions_mask", schema.Scalar()),
)
self.check_create_net_spec(
extractor, expected_input_record, expected_output_record
)
def _test_create_net_sarsa_parametric_action(self, normalize):
extractor = TrainingFeatureExtractor(
state_normalization_parameters=self.get_state_normalization_parameters(),
action_normalization_parameters=self.get_action_normalization_parameters(),
include_possible_actions=False,
normalize=normalize,
max_num_actions=2,
)
expected_input_record = schema.Struct(
("state_features", map_schema()),
("next_state_features", map_schema()),
("action", map_schema()),
("next_action", map_schema()),
("not_terminal", schema.Scalar()),
)
expected_output_record = schema.Struct(
("state_features", schema.Scalar()),
("next_state_features", schema.Scalar()),
("action", schema.Scalar()),
("next_action", schema.Scalar()),
("not_terminal", schema.Scalar()),
)
self.check_create_net_spec(
extractor, expected_input_record, expected_output_record
)
def _test_create_net_sarsa_parametric_action(self, normalize):
extractor = TrainingFeatureExtractor(
state_normalization_parameters=self.
get_state_normalization_parameters(),
action_normalization_parameters=self.
get_action_normalization_parameters(),
include_possible_actions=False,
normalize=normalize,
max_num_actions=2,
)
expected_input_record = schema.Struct(
("state_features", map_schema()),
("next_state_features", map_schema()),
("action", map_schema()),
("next_action", map_schema()),
("not_terminal", schema.Scalar()),
)
expected_output_record = schema.Struct(
("state_features", schema.Scalar()),
("next_state_features", schema.Scalar()),
("action", schema.Scalar()),
("next_action", schema.Scalar()),
("not_terminal", schema.Scalar()),
)
self.check_create_net_spec(extractor, expected_input_record,
expected_output_record)
def test_create_net_max_q_discrete_action(self):
extractor = TrainingFeatureExtractor(
state_normalization_parameters=self.
get_state_normalization_parameters(),
include_possible_actions=True,
max_num_actions=2,
)
expected_input_record = schema.Struct(
("state_features", map_schema()),
("next_state_features", map_schema()),
("action", schema.Scalar()),
("next_action", schema.Scalar()),
("not_terminal", schema.Scalar()),
("possible_actions_mask", schema.List(schema.Scalar())),
("possible_next_actions_mask", schema.List(schema.Scalar())),
)
expected_output_record = schema.Struct(
("state_features", schema.Scalar()),
("next_state_features", schema.Scalar()),
("action", schema.Scalar()),
("next_action", schema.Scalar()),
("not_terminal", schema.Scalar()),
("possible_actions_mask", schema.Scalar()),
("possible_next_actions_mask", schema.Scalar()),
)
self.check_create_net_spec(extractor, expected_input_record,
expected_output_record)
def test_create_net_sarsa_no_action(self):
extractor = PredictorFeatureExtractor(
state_normalization_parameters=self.
get_state_normalization_parameters())
expected_input_record = schema.Struct(("float_features", map_schema()))
expected_output_record = schema.Struct(("state", schema.Scalar()))
self.check_create_net_spec(extractor, expected_input_record,
expected_output_record)
def _test_create_net_sarsa_no_action(self, normalize):
extractor = PredictorFeatureExtractor(
state_normalization_parameters=self.get_state_normalization_parameters(),
normalize=normalize,
)
expected_input_record = schema.Struct(("float_features", map_schema()))
expected_output_record = schema.Struct(("state", schema.Scalar()))
self.check_create_net_spec(
extractor, expected_input_record, expected_output_record
)
def test_create_net_sarsa_discrete_action(self):
extractor = TrainingFeatureExtractor(
state_normalization_parameters=self.
get_state_normalization_parameters(),
max_q_learning=False,
)
expected_input_record = schema.Struct(
("state_features", map_schema()),
("next_state_features", map_schema()),
("action", schema.Scalar()),
("next_action", schema.Scalar()),
)
expected_output_record = schema.Struct(
("state", schema.Scalar()),
("next_state", schema.Scalar()),
("action", schema.Scalar()),
("next_action", schema.Scalar()),
)
self.check_create_net_spec(extractor, expected_input_record,
expected_output_record)
def test_create_net_max_q_parametric_action(self):
extractor = TrainingFeatureExtractor(
state_normalization_parameters=self.get_state_normalization_parameters(),
action_normalization_parameters=self.get_action_normalization_parameters(),
max_q_learning=True,
)
expected_input_record = schema.Struct(
("state_features", map_schema()),
("next_state_features", map_schema()),
("action", map_schema()),
("possible_next_actions", schema.List(map_schema())),
)
expected_output_record = schema.Struct(
("state", schema.Scalar()),
("next_state", schema.Scalar()),
("action", schema.Scalar()),
("possible_next_actions", schema.List(schema.Scalar())),
)
self.check_create_net_spec(
extractor, expected_input_record, expected_output_record
)
