def _test_discrete_dqn_net_builder(
self,
chooser: DiscreteDQNNetBuilder__Union,
state_feature_config: Optional[rlt.ModelFeatureConfig] = None,
serving_module_class=DiscreteDqnPredictorWrapper,
) -> None:
builder = chooser.value
state_dim = 3
state_feature_config = state_feature_config or rlt.ModelFeatureConfig(
float_feature_infos=[
rlt.FloatFeatureInfo(name=f"f{i}", feature_id=i)
for i in range(state_dim)
])
state_dim = len(state_feature_config.float_feature_infos)
state_norm_params = {
fi.feature_id: NormalizationParameters(feature_type=CONTINUOUS,
mean=0.0,
stddev=1.0)
for fi in state_feature_config.float_feature_infos
}
action_names = ["L", "R"]
q_network = builder.build_q_network(state_feature_config,
state_norm_params,
len(action_names))
x = q_network.input_prototype()
y = q_network(x).q_values
self.assertEqual(y.shape, (1, 2))
serving_module = builder.build_serving_module(q_network,
state_norm_params,
action_names,
state_feature_config)
self.assertIsInstance(serving_module, serving_module_class)
def test_discrete_wrapper_with_id_list(self):
state_normalization_parameters = {i: _cont_norm() for i in range(1, 5)}
state_preprocessor = Preprocessor(state_normalization_parameters,
False)
action_dim = 2
state_feature_config = rlt.ModelFeatureConfig(
float_feature_infos=[
rlt.FloatFeatureInfo(name=str(i), feature_id=i)
for i in range(1, 5)
],
id_list_feature_configs=[
rlt.IdListFeatureConfig(name="A",
feature_id=10,
id_mapping_name="A_mapping")
],
id_mapping_config={"A_mapping": rlt.IdMapping(ids=[0, 1, 2])},
)
dqn = FullyConnectedDQNWithEmbedding(
state_dim=len(state_normalization_parameters),
action_dim=action_dim,
sizes=[16],
activations=["relu"],
model_feature_config=state_feature_config,
embedding_dim=8,
)
dqn_with_preprocessor = DiscreteDqnWithPreprocessorWithIdList(
dqn, state_preprocessor, state_feature_config)
action_names = ["L", "R"]
wrapper = DiscreteDqnPredictorWrapperWithIdList(
dqn_with_preprocessor, action_names, state_feature_config)
input_prototype = dqn_with_preprocessor.input_prototype()
output_action_names, q_values = wrapper(*input_prototype)
self.assertEqual(action_names, output_action_names)
self.assertEqual(q_values.shape, (1, 2))
feature_id_to_name = {
config.feature_id: config.name
for config in state_feature_config.id_list_feature_configs
}
state_id_list_features = {
feature_id_to_name[k]: v
for k, v in input_prototype[1].items()
}
expected_output = dqn(
rlt.PreprocessedState(state=rlt.PreprocessedFeatureVector(
float_features=state_preprocessor(*input_prototype[0]),
id_list_features=state_id_list_features,
))).q_values
self.assertTrue((expected_output == q_values).all())
def test_fully_connected_with_id_list(self):
# Intentionally used this long path to make sure we included it in __init__.py
chooser = DiscreteDQNNetBuilderChooser(
FullyConnectedWithEmbedding=discrete_dqn.fully_connected_with_embedding.FullyConnectedWithEmbedding.config_type()()
)
state_feature_config = rlt.ModelFeatureConfig(
float_feature_infos=[
rlt.FloatFeatureInfo(name=str(i), feature_id=i) for i in range(1, 5)
],
id_list_feature_configs=[
rlt.IdListFeatureConfig(
name="A", feature_id=10, id_mapping_name="A_mapping"
)
],
id_mapping_config={"A_mapping": rlt.IdMapping(ids=[0, 1, 2])},
)
self._test_discrete_dqn_net_builder(
chooser,
state_feature_config=state_feature_config,
serving_module_class=DiscreteDqnPredictorWrapperWithIdList,
)
def get_float_feature_infos(cls) -> List[rlt.FloatFeatureInfo]:
return [
rlt.FloatFeatureInfo(name="f{}".format(f_id), feature_id=f_id)
for f_id in [1001, 1002]
]
