def __init__(
self,
q_network,
q_network_target,
reward_network,
parameters: ContinuousActionModelParameters,
) -> None:
self.double_q_learning = parameters.rainbow.double_q_learning
self.minibatch_size = parameters.training.minibatch_size
DQNTrainerBase.__init__(
self,
parameters,
use_gpu=False,
additional_feature_types=None,
gradient_handler=None,
)
self.q_network = q_network
self.q_network_target = q_network_target
self._set_optimizer(parameters.training.optimizer)
self.q_network_optimizer = self.optimizer_func(
self.q_network.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
self.reward_network = reward_network
self.reward_network_optimizer = self.optimizer_func(
self.reward_network.parameters(),
lr=parameters.training.learning_rate)
def __init__(
self,
q_network,
q_network_target,
reward_network,
parameters: ContinuousActionModelParameters,
use_gpu: bool = False,
) -> None:
DQNTrainerBase.__init__(self, parameters, use_gpu=use_gpu)
self.double_q_learning = parameters.rainbow.double_q_learning
self.minibatch_size = parameters.training.minibatch_size
self.minibatches_per_step = parameters.training.minibatches_per_step or 1
self.q_network = q_network
self.q_network_target = q_network_target
self._set_optimizer(parameters.training.optimizer)
self.q_network_optimizer = self.optimizer_func(
self.q_network.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
self.reward_network = reward_network
self.reward_network_optimizer = self.optimizer_func(
self.reward_network.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
def __init__(
self,
q_network,
q_network_target,
reward_network,
parameters: ContinuousActionModelParameters,
) -> None:
self.double_q_learning = parameters.rainbow.double_q_learning
self.minibatch_size = parameters.training.minibatch_size
DQNTrainerBase.__init__(
self,
parameters,
use_gpu=False,
additional_feature_types=None,
gradient_handler=None,
)
self.q_network = q_network
self.q_network_target = q_network_target
self._set_optimizer(parameters.training.optimizer)
self.q_network_optimizer = self.optimizer_func(
self.q_network.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
self.reward_network = reward_network
self.reward_network_optimizer = self.optimizer_func(
self.reward_network.parameters(), lr=parameters.training.learning_rate
)
def __init__(
self,
q_network,
q_network_target,
reward_network,
parameters: DiscreteActionModelParameters,
use_gpu=False,
q_network_cpe=None,
q_network_cpe_target=None,
metrics_to_score=None,
) -> None:
self.double_q_learning = parameters.rainbow.double_q_learning
self.minibatch_size = parameters.training.minibatch_size
self._actions = parameters.actions if parameters.actions is not None else []
DQNTrainerBase.__init__(
self,
parameters,
use_gpu=use_gpu,
metrics_to_score=metrics_to_score,
gradient_handler=None,
actions=parameters.actions,
)
self.q_network = q_network
self.q_network_target = q_network_target
self._set_optimizer(parameters.training.optimizer)
self.q_network_optimizer = self.optimizer_func(
self.q_network.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
if self.calc_cpe_in_training:
assert reward_network is not None, "reward_network is required for CPE"
self.reward_network = reward_network
self.reward_network_optimizer = self.optimizer_func(
self.reward_network.parameters(),
lr=parameters.training.learning_rate)
assert (
q_network_cpe is not None and q_network_cpe_target is not None
), "q_network_cpe and q_network_cpe_target are required for CPE"
self.q_network_cpe = q_network_cpe
self.q_network_cpe_target = q_network_cpe_target
self.q_network_cpe_optimizer = self.optimizer_func(
self.q_network_cpe.parameters(),
lr=parameters.training.learning_rate)
num_output_nodes = len(self.metrics_to_score) * self.num_actions
self.reward_idx_offsets = torch.arange(
0, num_output_nodes, self.num_actions).type(self.dtypelong)
self.reward_boosts = torch.zeros([1,
len(self._actions)]).type(self.dtype)
if parameters.rl.reward_boost is not None:
for k in parameters.rl.reward_boost.keys():
i = self._actions.index(k)
self.reward_boosts[0, i] = parameters.rl.reward_boost[k]
def __init__(
self,
q_network,
q_network_target,
reward_network,
parameters: DiscreteActionModelParameters,
use_gpu=False,
) -> None:
self.double_q_learning = parameters.rainbow.double_q_learning
self.minibatch_size = parameters.training.minibatch_size
self._actions = parameters.actions if parameters.actions is not None else []
DQNTrainerBase.__init__(
self,
parameters,
use_gpu=use_gpu,
additional_feature_types=None,
gradient_handler=None,
)
self.q_network = q_network
self.q_network_target = q_network_target
self._set_optimizer(parameters.training.optimizer)
self.q_network_optimizer = self.optimizer_func(
self.q_network.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
self.reward_network = reward_network
self.reward_network_optimizer = self.optimizer_func(
self.reward_network.parameters(),
lr=parameters.training.learning_rate)
self.reward_boosts = torch.zeros([1,
len(self._actions)]).type(self.dtype)
if parameters.rl.reward_boost is not None:
for k in parameters.rl.reward_boost.keys():
i = self._actions.index(k)
self.reward_boosts[0, i] = parameters.rl.reward_boost[k]
def __init__(
self,
q_network,
q_network_target,
reward_network,
parameters: DiscreteActionModelParameters,
use_gpu=False,
q_network_cpe=None,
q_network_cpe_target=None,
metrics_to_score=None,
imitator=None,
) -> None:
DQNTrainerBase.__init__(
self,
parameters,
use_gpu=use_gpu,
metrics_to_score=metrics_to_score,
actions=parameters.actions,
)
self.double_q_learning = parameters.rainbow.double_q_learning
self.minibatch_size = parameters.training.minibatch_size
self.minibatches_per_step = parameters.training.minibatches_per_step or 1
self._actions = parameters.actions if parameters.actions is not None else []
self.q_network = q_network
self.q_network_target = q_network_target
self._set_optimizer(parameters.training.optimizer)
self.q_network_optimizer = self.optimizer_func(
self.q_network.parameters(),
lr=parameters.training.learning_rate,
weight_decay=parameters.training.l2_decay,
)
if self.calc_cpe_in_training:
assert reward_network is not None, "reward_network is required for CPE"
self.reward_network = reward_network
self.reward_network_optimizer = self.optimizer_func(
self.reward_network.parameters(),
lr=parameters.training.learning_rate)
assert (
q_network_cpe is not None and q_network_cpe_target is not None
), "q_network_cpe and q_network_cpe_target are required for CPE"
self.q_network_cpe = q_network_cpe
self.q_network_cpe_target = q_network_cpe_target
self.q_network_cpe_optimizer = self.optimizer_func(
self.q_network_cpe.parameters(),
lr=parameters.training.learning_rate)
num_output_nodes = len(self.metrics_to_score) * self.num_actions
self.reward_idx_offsets = torch.arange(
0,
num_output_nodes,
self.num_actions,
device=self.device,
dtype=torch.long,
)
else:
self.reward_network = None
self.reward_boosts = torch.zeros([1, len(self._actions)],
device=self.device)
if parameters.rl.reward_boost is not None:
for k in parameters.rl.reward_boost.keys():
i = self._actions.index(k)
self.reward_boosts[0, i] = parameters.rl.reward_boost[k]
# Batch constrained q-learning
self.bcq = parameters.rainbow.bcq
if self.bcq:
self.bcq_drop_threshold = parameters.rainbow.bcq_drop_threshold
self.bcq_imitator = imitator