make_trainer

torchrl.trainers.helpers.make_trainer(collector: DataCollectorBase, loss_module: LossModule, recorder: Optional[EnvBase] = None, target_net_updater: Optional[TargetNetUpdater] = None, policy_exploration: Optional[Union[TensorDictModuleWrapper, TensorDictModule]] = None, replay_buffer: Optional[ReplayBuffer] = None, logger: Optional[Logger] = None, cfg: DictConfig = None) → Trainer

Creates a Trainer instance given its constituents.

Parameters:

• collector (DataCollectorBase) – A data collector to be used to collect data.

• loss_module (LossModule) – A TorchRL loss module

• recorder (EnvBase, optional) – a recorder environment. If None, the trainer will train the policy without testing it.

• target_net_updater (TargetNetUpdater, optional) – A target network update object.

• policy_exploration (TDModule or TensorDictModuleWrapper, optional) – a policy to be used for recording and exploration updates (should be synced with the learnt policy).

• replay_buffer (ReplayBuffer, optional) – a replay buffer to be used to collect data.

• logger (Logger, optional) – a Logger to be used for logging.

• cfg (DictConfig, optional) – a DictConfig containing the arguments of the script. If None, the default arguments are used.

Returns:

A trainer built with the input objects. The optimizer is built by this helper function using the cfg provided.

Examples

>>> import torch
>>> import tempfile
>>> from torchrl.trainers.loggers import TensorboardLogger
>>> from torchrl.trainers import Trainer
>>> from torchrl.envs import EnvCreator
>>> from torchrl.collectors.collectors import SyncDataCollector
>>> from torchrl.data import TensorDictReplayBuffer
>>> from torchrl.envs.libs.gym import GymEnv
>>> from torchrl.modules import TensorDictModuleWrapper, SafeModule, ValueOperator, EGreedyWrapper
>>> from torchrl.objectives.common import LossModule
>>> from torchrl.objectives.utils import TargetNetUpdater
>>> from torchrl.objectives import DDPGLoss
>>> env_maker = EnvCreator(lambda: GymEnv("Pendulum-v0"))
>>> env_proof = env_maker()
>>> obs_spec = env_proof.observation_spec
>>> action_spec = env_proof.action_spec
>>> net = torch.nn.Linear(env_proof.observation_spec.shape[-1], action_spec.shape[-1])
>>> net_value = torch.nn.Linear(env_proof.observation_spec.shape[-1], 1)  # for the purpose of testing
>>> policy = SafeModule(action_spec, net, in_keys=["observation"], out_keys=["action"])
>>> value = ValueOperator(net_value, in_keys=["observation"], out_keys=["state_action_value"])
>>> collector = SyncDataCollector(env_maker, policy, total_frames=100)
>>> loss_module = DDPGLoss(policy, value, gamma=0.99)
>>> recorder = env_proof
>>> target_net_updater = None
>>> policy_exploration = EGreedyWrapper(policy)
>>> replay_buffer = TensorDictReplayBuffer()
>>> dir = tempfile.gettempdir()
>>> logger = TensorboardLogger(exp_name=dir)
>>> trainer = make_trainer(collector, loss_module, recorder, target_net_updater, policy_exploration,
...    replay_buffer, logger)
>>> print(trainer)