class DiscreteDQNPredictorPolicy(Policy):
def __init__(self, wrapped_dqn_predictor,
rl_parameters: Optional[RLParameters]):
if rl_parameters and rl_parameters.softmax_policy:
self.sampler = SoftmaxActionSampler(
temperature=rl_parameters.temperature)
else:
self.sampler = GreedyActionSampler()
self.scorer = discrete_dqn_serving_scorer(
q_network=DiscreteDqnPredictorUnwrapper(wrapped_dqn_predictor))
# pyre-fixme[56]: Decorator `torch.no_grad(...)` could not be called, because
# its type `no_grad` is not callable.
@torch.no_grad()
def act(
self,
obs: Union[rlt.ServingFeatureData, Tuple[torch.Tensor, torch.Tensor]],
possible_actions_mask: Optional[np.ndarray],
) -> rlt.ActorOutput:
"""Input is either state_with_presence, or
ServingFeatureData (in the case of sparse features)"""
assert isinstance(obs, tuple)
if isinstance(obs, rlt.ServingFeatureData):
state: rlt.ServingFeatureData = obs
else:
state = rlt.ServingFeatureData(
float_features_with_presence=obs,
id_list_features={},
id_score_list_features={},
)
scores = self.scorer(state, possible_actions_mask)
return self.sampler.sample_action(scores).cpu().detach()
class DiscreteDQNPredictorPolicy(Policy):
def __init__(self, wrapped_dqn_predictor):
self.sampler = GreedyActionSampler()
self.scorer = discrete_dqn_serving_scorer(
q_network=DiscreteDqnPredictorUnwrapper(wrapped_dqn_predictor)
)
@torch.no_grad()
def act(
self, obs: Union[rlt.ServingFeatureData, Tuple[torch.Tensor, torch.Tensor]]
) -> rlt.ActorOutput:
""" Input is either state_with_presence, or
ServingFeatureData (in the case of sparse features) """
assert isinstance(obs, tuple)
if isinstance(obs, rlt.ServingFeatureData):
state: rlt.ServingFeatureData = obs
else:
state = rlt.ServingFeatureData(
float_features_with_presence=obs,
id_list_features={},
id_score_list_features={},
)
scores = self.scorer(state)
return self.sampler.sample_action(scores).cpu().detach()