def __init__(
self,
q_network,
q_network_target,
use_gpu: bool = False,
# Start SlateQTrainerParameters
rl: rlp.RLParameters = field( # noqa: B008
default_factory=lambda: rlp.RLParameters(maxq_learning=False)),
optimizer: Optimizer__Union = field( # noqa: B008
default_factory=Optimizer__Union.default),
single_selection: bool = True,
minibatch_size: int = 1024,
evaluation: rlp.EvaluationParameters = field( # noqa: B008
default_factory=lambda: rlp.EvaluationParameters(
calc_cpe_in_training=False)),
) -> None:
super().__init__(rl, use_gpu=use_gpu)
self.minibatches_per_step = 1
self.minibatch_size = minibatch_size
self.single_selection = single_selection
self.q_network = q_network
self.q_network_target = q_network_target
self.q_network_optimizer = optimizer.make_optimizer(
self.q_network.parameters())
class SlateQTrainerParameters:
rl: rlp.RLParameters = field(
default_factory=lambda: rlp.RLParameters(maxq_learning=False))
optimizer: str = "ADAM"
learning_rate: float = 0.001
minibatch_size: int = 1024
evaluation: rlp.EvaluationParameters = field(
default_factory=lambda: rlp.EvaluationParameters(calc_cpe_in_training=
False))
def test_json_serialize_basic(self):
damp = rlp.DiscreteActionModelParameters(
actions=["foo", "bar"],
rl=rlp.RLParameters(),
training=rlp.TrainingParameters(),
rainbow=rlp.RainbowDQNParameters(double_q_learning=False, categorical=True),
state_feature_params=None,
target_action_distribution=[1.0, 2.0],
evaluation=rlp.EvaluationParameters(),
)
self.assertEqual(
damp,
json_to_object(object_to_json(damp), rlp.DiscreteActionModelParameters),
)
def __init__(
self,
q_network,
q_network_target,
# Start SlateQTrainerParameters
rl: rlp.RLParameters = field( # noqa: B008
default_factory=lambda: rlp.RLParameters(maxq_learning=False)
),
optimizer: Optimizer__Union = field( # noqa: B008
default_factory=Optimizer__Union.default
),
single_selection: bool = True,
minibatch_size: int = 1024,
evaluation: rlp.EvaluationParameters = field( # noqa: B008
default_factory=lambda: rlp.EvaluationParameters(calc_cpe_in_training=False)
),
) -> None:
"""
Args:
q_network: states, action -> q-value
rl (optional): an instance of the RLParameter class, which
defines relevant hyperparameters
optimizer (optional): the optimizer class and
optimizer hyperparameters for the q network(s) optimizer
single_selection (optional): TBD
minibatch_size (optional): the size of the minibatch
evaluation (optional): TBD
"""
super().__init__()
self.rl_parameters = rl
self.single_selection = single_selection
self.q_network = q_network
self.q_network_target = q_network_target
self.q_network_optimizer = optimizer