def get_modular_sarsa_trainer_reward_boost(
self,
environment,
reward_shape,
dueling,
use_gpu=False,
use_all_avail_gpus=False,
clip_grad_norm=None,
):
parameters = self.get_sarsa_parameters(environment, reward_shape,
dueling, clip_grad_norm)
q_network = FullyConnectedDQN(
state_dim=get_num_output_features(environment.normalization),
action_dim=len(environment.ACTIONS),
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
reward_network = FullyConnectedDQN(
state_dim=get_num_output_features(environment.normalization),
action_dim=len(environment.ACTIONS),
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
q_network_cpe = FullyConnectedDQN(
state_dim=get_num_output_features(environment.normalization),
action_dim=len(environment.ACTIONS),
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
if use_gpu:
q_network = q_network.cuda()
reward_network = reward_network.cuda()
q_network_cpe = q_network_cpe.cuda()
if use_all_avail_gpus:
q_network = q_network.get_distributed_data_parallel_model()
reward_network = reward_network.get_distributed_data_parallel_model(
)
q_network_cpe = q_network_cpe.get_distributed_data_parallel_model(
)
q_network_target = q_network.get_target_network()
q_network_cpe_target = q_network_cpe.get_target_network()
trainer = DQNTrainer(
q_network,
q_network_target,
reward_network,
parameters,
use_gpu,
q_network_cpe=q_network_cpe,
q_network_cpe_target=q_network_cpe_target,
)
return trainer
def create_dqn_trainer_from_params(
model: DiscreteActionModelParameters,
normalization_parameters: Dict[int, NormalizationParameters],
use_gpu: bool = False,
use_all_avail_gpus: bool = False,
metrics_to_score=None,
):
metrics_to_score = metrics_to_score or []
if model.rainbow.quantile:
q_network = QuantileDQN(
state_dim=get_num_output_features(normalization_parameters),
action_dim=len(model.actions),
num_atoms=model.rainbow.num_atoms,
sizes=model.training.layers[1:-1],
activations=model.training.activations[:-1],
dropout_ratio=model.training.dropout_ratio,
)
elif model.rainbow.categorical:
q_network = CategoricalDQN( # type: ignore
state_dim=get_num_output_features(normalization_parameters),
action_dim=len(model.actions),
num_atoms=model.rainbow.num_atoms,
qmin=model.rainbow.qmin,
qmax=model.rainbow.qmax,
sizes=model.training.layers[1:-1],
activations=model.training.activations[:-1],
dropout_ratio=model.training.dropout_ratio,
use_gpu=use_gpu,
)
elif model.rainbow.dueling_architecture:
q_network = DuelingQNetwork( # type: ignore
layers=[get_num_output_features(normalization_parameters)] +
model.training.layers[1:-1] + [len(model.actions)],
activations=model.training.activations,
)
else:
q_network = FullyConnectedDQN( # type: ignore
state_dim=get_num_output_features(normalization_parameters),
action_dim=len(model.actions),
sizes=model.training.layers[1:-1],
activations=model.training.activations[:-1],
dropout_ratio=model.training.dropout_ratio,
)
if use_gpu and torch.cuda.is_available():
q_network = q_network.cuda()
q_network_target = q_network.get_target_network()
reward_network, q_network_cpe, q_network_cpe_target = None, None, None
if model.evaluation.calc_cpe_in_training:
# Metrics + reward
num_output_nodes = (len(metrics_to_score) + 1) * len(model.actions)
reward_network = FullyConnectedDQN(
state_dim=get_num_output_features(normalization_parameters),
action_dim=num_output_nodes,
sizes=model.training.layers[1:-1],
activations=model.training.activations[:-1],
dropout_ratio=model.training.dropout_ratio,
)
q_network_cpe = FullyConnectedDQN(
state_dim=get_num_output_features(normalization_parameters),
action_dim=num_output_nodes,
sizes=model.training.layers[1:-1],
activations=model.training.activations[:-1],
dropout_ratio=model.training.dropout_ratio,
)
if use_gpu and torch.cuda.is_available():
reward_network.cuda()
q_network_cpe.cuda()
q_network_cpe_target = q_network_cpe.get_target_network()
if (use_all_avail_gpus and not model.rainbow.categorical
and not model.rainbow.quantile):
q_network = q_network.get_distributed_data_parallel_model()
reward_network = (reward_network.get_distributed_data_parallel_model()
if reward_network else None)
q_network_cpe = (q_network_cpe.get_distributed_data_parallel_model()
if q_network_cpe else None)
if model.rainbow.quantile:
assert (not use_all_avail_gpus
), "use_all_avail_gpus not implemented for distributional RL"
return QRDQNTrainer(
q_network,
q_network_target,
model,
use_gpu,
metrics_to_score=metrics_to_score,
)
elif model.rainbow.categorical:
assert (not use_all_avail_gpus
), "use_all_avail_gpus not implemented for distributional RL"
return C51Trainer(
q_network,
q_network_target,
model,
use_gpu,
metrics_to_score=metrics_to_score,
)
else:
return DQNTrainer(
q_network,
q_network_target,
reward_network,
model,
use_gpu,
q_network_cpe=q_network_cpe,
q_network_cpe_target=q_network_cpe_target,
metrics_to_score=metrics_to_score,
)
def get_modular_sarsa_trainer_reward_boost(
self,
environment,
reward_shape,
dueling,
categorical,
quantile,
use_gpu=False,
use_all_avail_gpus=False,
clip_grad_norm=None,
):
assert not quantile or not categorical
parameters = self.get_sarsa_parameters(environment, reward_shape,
dueling, categorical, quantile,
clip_grad_norm)
if quantile:
if dueling:
q_network = DuelingQuantileDQN(
layers=[
get_num_output_features(environment.normalization)
] + parameters.training.layers[1:-1] +
[len(environment.ACTIONS)],
activations=parameters.training.activations,
num_atoms=parameters.rainbow.num_atoms,
)
else:
q_network = QuantileDQN(
state_dim=get_num_output_features(
environment.normalization),
action_dim=len(environment.ACTIONS),
num_atoms=parameters.rainbow.num_atoms,
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
elif categorical:
assert not dueling
q_network = CategoricalDQN(
state_dim=get_num_output_features(environment.normalization),
action_dim=len(environment.ACTIONS),
num_atoms=parameters.rainbow.num_atoms,
qmin=-100,
qmax=200,
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
else:
if dueling:
q_network = DuelingQNetwork(
layers=[
get_num_output_features(environment.normalization)
] + parameters.training.layers[1:-1] +
[len(environment.ACTIONS)],
activations=parameters.training.activations,
)
else:
q_network = FullyConnectedDQN(
state_dim=get_num_output_features(
environment.normalization),
action_dim=len(environment.ACTIONS),
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
q_network_cpe, q_network_cpe_target, reward_network = None, None, None
if parameters.evaluation and parameters.evaluation.calc_cpe_in_training:
q_network_cpe = FullyConnectedDQN(
state_dim=get_num_output_features(environment.normalization),
action_dim=len(environment.ACTIONS),
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
q_network_cpe_target = q_network_cpe.get_target_network()
reward_network = FullyConnectedDQN(
state_dim=get_num_output_features(environment.normalization),
action_dim=len(environment.ACTIONS),
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
if use_gpu:
q_network = q_network.cuda()
if parameters.evaluation.calc_cpe_in_training:
reward_network = reward_network.cuda()
q_network_cpe = q_network_cpe.cuda()
q_network_cpe_target = q_network_cpe_target.cuda()
if use_all_avail_gpus and not categorical:
q_network = q_network.get_distributed_data_parallel_model()
reward_network = reward_network.get_distributed_data_parallel_model(
)
q_network_cpe = q_network_cpe.get_distributed_data_parallel_model(
)
q_network_cpe_target = (
q_network_cpe_target.get_distributed_data_parallel_model())
if quantile:
trainer = QRDQNTrainer(
q_network,
q_network.get_target_network(),
parameters,
use_gpu,
reward_network=reward_network,
q_network_cpe=q_network_cpe,
q_network_cpe_target=q_network_cpe_target,
)
elif categorical:
trainer = C51Trainer(q_network, q_network.get_target_network(),
parameters, use_gpu)
else:
parameters = DQNTrainerParameters.from_discrete_action_model_parameters(
parameters)
trainer = DQNTrainer(
q_network,
q_network.get_target_network(),
reward_network,
parameters,
use_gpu,
q_network_cpe=q_network_cpe,
q_network_cpe_target=q_network_cpe_target,
)
return trainer
def get_modular_sarsa_trainer_reward_boost(
self,
environment,
reward_shape,
dueling,
categorical,
quantile,
use_gpu=False,
use_all_avail_gpus=False,
clip_grad_norm=None,
):
parameters = self.get_sarsa_parameters(environment, reward_shape,
dueling, categorical, quantile,
clip_grad_norm)
if quantile:
q_network = QuantileDQN(
state_dim=get_num_output_features(environment.normalization),
action_dim=len(environment.ACTIONS),
num_atoms=50,
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
parameters.rainbow.num_atoms = 50
elif categorical:
q_network = CategoricalDQN(
state_dim=get_num_output_features(environment.normalization),
action_dim=len(environment.ACTIONS),
num_atoms=51,
qmin=-100,
qmax=200,
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
else:
q_network = FullyConnectedDQN(
state_dim=get_num_output_features(environment.normalization),
action_dim=len(environment.ACTIONS),
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
q_network_cpe = FullyConnectedDQN(
state_dim=get_num_output_features(environment.normalization),
action_dim=len(environment.ACTIONS),
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
reward_network = FullyConnectedDQN(
state_dim=get_num_output_features(environment.normalization),
action_dim=len(environment.ACTIONS),
sizes=parameters.training.layers[1:-1],
activations=parameters.training.activations[:-1],
)
if use_gpu:
q_network = q_network.cuda()
if not categorical and not quantile:
reward_network = reward_network.cuda()
q_network_cpe = q_network_cpe.cuda()
if use_all_avail_gpus and not categorical:
q_network = q_network.get_distributed_data_parallel_model()
reward_network = reward_network.get_distributed_data_parallel_model(
)
q_network_cpe = q_network_cpe.get_distributed_data_parallel_model(
)
if quantile:
trainer = QRDQNTrainer(q_network, q_network.get_target_network(),
parameters, use_gpu)
elif categorical:
trainer = C51Trainer(q_network, q_network.get_target_network(),
parameters, use_gpu)
else:
trainer = DQNTrainer(
q_network,
q_network.get_target_network(),
reward_network,
parameters,
use_gpu,
q_network_cpe=q_network_cpe,
q_network_cpe_target=q_network_cpe.get_target_network(),
)
return trainer
def create_park_dqn_trainer_from_params(
model: DiscreteActionModelParameters,
normalization_parameters: Dict[int, NormalizationParameters],
use_gpu: bool = False,
use_all_avail_gpus: bool = False,
metrics_to_score=None,
env=None):
metrics_to_score = metrics_to_score or []
if model.rainbow.dueling_architecture:
q_network = DuelingQNetwork(
layers=[get_num_output_features(normalization_parameters)] +
model.training.layers[1:-1] + [len(model.actions)],
activations=model.training.activations,
)
else:
q_network = FullyConnectedDQN(
state_dim=get_num_output_features(normalization_parameters),
action_dim=len(model.actions),
sizes=model.training.layers[1:-1],
activations=model.training.activations[:-1],
dropout_ratio=model.training.dropout_ratio,
)
if use_gpu and torch.cuda.is_available():
q_network = q_network.cuda()
q_network_target = q_network.get_target_network()
reward_network, q_network_cpe, q_network_cpe_target = None, None, None
if model.evaluation.calc_cpe_in_training:
# Metrics + reward
num_output_nodes = (len(metrics_to_score) + 1) * len(model.actions)
reward_network = FullyConnectedDQN(
state_dim=get_num_output_features(normalization_parameters),
action_dim=num_output_nodes,
sizes=model.training.layers[1:-1],
activations=model.training.activations[:-1],
dropout_ratio=model.training.dropout_ratio,
)
q_network_cpe = FullyConnectedDQN(
state_dim=get_num_output_features(normalization_parameters),
action_dim=num_output_nodes,
sizes=model.training.layers[1:-1],
activations=model.training.activations[:-1],
dropout_ratio=model.training.dropout_ratio,
)
if use_gpu and torch.cuda.is_available():
reward_network.cuda()
q_network_cpe.cuda()
q_network_cpe_target = q_network_cpe.get_target_network()
if use_all_avail_gpus:
q_network = q_network.get_distributed_data_parallel_model()
reward_network = (reward_network.get_distributed_data_parallel_model()
if reward_network else None)
q_network_cpe = (q_network_cpe.get_distributed_data_parallel_model()
if q_network_cpe else None)
return ParkDQNTrainer(q_network,
q_network_target,
reward_network,
model,
use_gpu,
q_network_cpe=q_network_cpe,
q_network_cpe_target=q_network_cpe_target,
metrics_to_score=metrics_to_score,
env=env)