def configure_optimizers(self):
optimizers = []
optimizers.append(
self.q_network_optimizer.make_optimizer_scheduler(
self.q_network.parameters()))
if self.calc_cpe_in_training:
optimizers.append(
self.reward_network_optimizer.make_optimizer_scheduler(
self.reward_network.parameters()))
optimizers.append(
self.q_network_cpe_optimizer.make_optimizer_scheduler(
self.q_network_cpe.parameters()))
# soft-update
target_params = list(self.q_network_target.parameters())
source_params = list(self.q_network.parameters())
if self.calc_cpe_in_training:
target_params += list(self.q_network_cpe_target.parameters())
source_params += list(self.q_network_cpe.parameters())
optimizers.append(
SoftUpdate.make_optimizer_scheduler(target_params,
source_params,
tau=self.tau))
return optimizers
def configure_optimizers(self):
optimizers = []
target_params = list(self.q_network_target.parameters())
source_params = list(self.q_network.parameters())
optimizers.append(
self.q_network_optimizer.make_optimizer_scheduler(
self.q_network.parameters()))
if self.calc_cpe_in_training:
(
cpe_target_params,
cpe_source_params,
cpe_optimizers,
) = self._configure_cpe_optimizers()
target_params += cpe_target_params
source_params += cpe_source_params
optimizers += cpe_optimizers
optimizers.append(
SoftUpdate.make_optimizer_scheduler(target_params,
source_params,
tau=self.tau))
return optimizers
def configure_optimizers(self):
optimizers = []
optimizers.append(
self.q_network_optimizer.make_optimizer_scheduler(
self.q1_network.parameters()))
if self.q2_network:
optimizers.append(
self.q_network_optimizer.make_optimizer_scheduler(
self.q2_network.parameters()))
optimizers.append(
self.actor_network_optimizer.make_optimizer_scheduler(
self.actor_network.parameters()))
# soft-update
target_params = list(self.q1_network_target.parameters())
source_params = list(self.q1_network.parameters())
if self.q2_network:
target_params += list(self.q2_network_target.parameters())
source_params += list(self.q2_network.parameters())
target_params += list(self.actor_network_target.parameters())
source_params += list(self.actor_network.parameters())
optimizers.append(
SoftUpdate.make_optimizer_scheduler(target_params,
source_params,
tau=self.tau))
return optimizers
def configure_optimizers(self):
optimizers = []
optimizers.append(
self.q_network_optimizer.make_optimizer_scheduler(
self.q1_network.parameters()
)
)
if self.q2_network:
optimizers.append(
self.q_network_optimizer.make_optimizer_scheduler(
self.q2_network.parameters()
)
)
optimizers.append(
self.actor_network_optimizer.make_optimizer_scheduler(
self.actor_network.parameters()
)
)
if self.alpha_optimizer is not None:
optimizers.append(
self.alpha_optimizer.make_optimizer_scheduler([self.log_alpha])
)
if self.value_network:
optimizers.append(
self.value_network_optimizer.make_optimizer_scheduler(
self.value_network.parameters()
)
)
# soft-update
if self.value_network:
target_params = self.value_network_target.parameters()
source_params = self.value_network.parameters()
else:
target_params = list(self.q1_network_target.parameters())
source_params = list(self.q1_network.parameters())
if self.q2_network:
target_params += list(self.q2_network_target.parameters())
source_params += list(self.q2_network.parameters())
optimizers.append(
SoftUpdate.make_optimizer_scheduler(
target_params, source_params, tau=self.tau
)
)
return optimizers
