def sparse_input_prototype(
model: ModelBase,
state_preprocessor: Preprocessor,
state_feature_config: rlt.ModelFeatureConfig,
):
name2id = state_feature_config.name2id
model_prototype = model.input_prototype()
# Terrible hack to make JIT tracing works. Python dict doesn't have type
# so we need to insert something so JIT tracer can infer the type.
state_id_list_features = {
42: (torch.zeros(1, dtype=torch.long), torch.tensor([], dtype=torch.long))
}
state_id_score_list_features = {
42: (
torch.zeros(1, dtype=torch.long),
torch.tensor([], dtype=torch.long),
torch.tensor([], dtype=torch.float),
)
}
if isinstance(model_prototype, rlt.FeatureData):
if model_prototype.id_list_features:
state_id_list_features = {
name2id[k]: v for k, v in model_prototype.id_list_features.items()
}
if model_prototype.id_score_list_features:
state_id_score_list_features = {
name2id[k]: v for k, v in model_prototype.id_score_list_features.items()
}
input = rlt.ServingFeatureData(
float_features_with_presence=state_preprocessor.input_prototype(),
id_list_features=state_id_list_features,
id_score_list_features=state_id_score_list_features,
)
return (input,)
def sparse_input_prototype(
model: ModelBase,
state_preprocessor: Preprocessor,
state_feature_config: rlt.ModelFeatureConfig,
):
name2id = state_feature_config.name2id
model_prototype = model.input_prototype()
# Terrible hack to make JIT tracing works. Python dict doesn't have type
# so we need to insert something so JIT tracer can infer the type.
state_id_list_features = FAKE_STATE_ID_LIST_FEATURES
state_id_score_list_features = FAKE_STATE_ID_SCORE_LIST_FEATURES
if isinstance(model_prototype, rlt.FeatureData):
if model_prototype.id_list_features:
state_id_list_features = {
name2id[k]: v
for k, v in model_prototype.id_list_features.items()
}
if model_prototype.id_score_list_features:
state_id_score_list_features = {
name2id[k]: v
for k, v in model_prototype.id_score_list_features.items()
}
input = rlt.ServingFeatureData(
float_features_with_presence=state_preprocessor.input_prototype(),
id_list_features=state_id_list_features,
id_score_list_features=state_id_score_list_features,
)
return (input, )
def __init__(
self,
*,
shared_network: ModelBase,
advantage_network: ModelBase,
value_network: ModelBase,
) -> None:
"""
Dueling Q-Network Architecture: https://arxiv.org/abs/1511.06581
"""
super().__init__()
self.shared_network = shared_network
input_prototype = shared_network.input_prototype()
assert isinstance(
input_prototype, rlt.FeatureData
), f"shared_network should expect FeatureData as input"
self.advantage_network = advantage_network
self.value_network = value_network
_check_connection(self)
self._name = "unnamed"
def __init__(
self,
*,
shared_network: ModelBase,
advantage_network: ModelBase,
value_network: ModelBase,
) -> None:
"""
Dueling Q-Network Architecture: https://arxiv.org/abs/1511.06581
"""
super().__init__()
advantage_network_input = advantage_network.input_prototype()
assert (isinstance(advantage_network_input, tuple)
and len(advantage_network_input) == 2)
assert advantage_network_input[0].has_float_features_only
self.shared_network = shared_network
self.advantage_network = advantage_network
self.value_network = value_network
_check_connection(self)
self._name = "unnamed"
