Source code for ray.rllib.policy.policy_template

import gym
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, \
    TYPE_CHECKING, Union

from ray.rllib.models.catalog import ModelCatalog
from ray.rllib.models.jax.jax_modelv2 import JAXModelV2
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.torch_policy import TorchPolicy
from ray.rllib.utils import add_mixins, force_list, NullContextManager
from ray.rllib.utils.annotations import override, DeveloperAPI
from ray.rllib.utils.framework import try_import_torch, try_import_jax
from ray.rllib.utils.torch_ops import convert_to_non_torch_type
from ray.rllib.utils.typing import ModelGradients, TensorType, \
    TrainerConfigDict

if TYPE_CHECKING:
    from ray.rllib.evaluation import MultiAgentEpisode  # noqa

jax, _ = try_import_jax()
torch, _ = try_import_torch()


# TODO: (sven) Unify this with `build_tf_policy` as well.
[docs]@DeveloperAPI def build_policy_class( name: str, framework: str, *, loss_fn: Optional[Callable[[ Policy, ModelV2, Type[TorchDistributionWrapper], SampleBatch ], Union[TensorType, List[TensorType]]]], get_default_config: Optional[Callable[[], TrainerConfigDict]] = None, stats_fn: Optional[Callable[[Policy, SampleBatch], Dict[ str, TensorType]]] = None, postprocess_fn: Optional[Callable[[ Policy, SampleBatch, Optional[Dict[Any, SampleBatch]], Optional[ "MultiAgentEpisode"] ], SampleBatch]] = None, extra_action_out_fn: Optional[Callable[[ Policy, Dict[str, TensorType], List[TensorType], ModelV2, TorchDistributionWrapper ], Dict[str, TensorType]]] = None, extra_grad_process_fn: Optional[Callable[[ Policy, "torch.optim.Optimizer", TensorType ], Dict[str, TensorType]]] = None, # TODO: (sven) Replace "fetches" with "process". extra_learn_fetches_fn: Optional[Callable[[Policy], Dict[ str, TensorType]]] = None, optimizer_fn: Optional[Callable[[Policy, TrainerConfigDict], "torch.optim.Optimizer"]] = None, validate_spaces: Optional[Callable[ [Policy, gym.Space, gym.Space, TrainerConfigDict], None]] = None, before_init: Optional[Callable[ [Policy, gym.Space, gym.Space, TrainerConfigDict], None]] = None, before_loss_init: Optional[Callable[[ Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict ], None]] = None, after_init: Optional[Callable[ [Policy, gym.Space, gym.Space, TrainerConfigDict], None]] = None, _after_loss_init: Optional[Callable[[ Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict ], None]] = None, action_sampler_fn: Optional[Callable[[TensorType, List[ TensorType]], Tuple[TensorType, TensorType]]] = None, action_distribution_fn: Optional[Callable[[ Policy, ModelV2, TensorType, TensorType, TensorType ], Tuple[TensorType, type, List[TensorType]]]] = None, make_model: Optional[Callable[[ Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict ], ModelV2]] = None, make_model_and_action_dist: Optional[Callable[[ Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict ], Tuple[ModelV2, Type[TorchDistributionWrapper]]]] = None, compute_gradients_fn: Optional[Callable[[Policy, SampleBatch], Tuple[ ModelGradients, dict]]] = None, apply_gradients_fn: Optional[Callable[ [Policy, "torch.optim.Optimizer"], None]] = None, mixins: Optional[List[type]] = None, get_batch_divisibility_req: Optional[Callable[[Policy], int]] = None ) -> Type[TorchPolicy]: """Helper function for creating a new Policy class at runtime. Supports frameworks JAX and PyTorch. Args: name (str): name of the policy (e.g., "PPOTorchPolicy") framework (str): Either "jax" or "torch". loss_fn (Optional[Callable[[Policy, ModelV2, Type[TorchDistributionWrapper], SampleBatch], Union[TensorType, List[TensorType]]]]): Callable that returns a loss tensor. get_default_config (Optional[Callable[[None], TrainerConfigDict]]): Optional callable that returns the default config to merge with any overrides. If None, uses only(!) the user-provided PartialTrainerConfigDict as dict for this Policy. postprocess_fn (Optional[Callable[[Policy, SampleBatch, Optional[Dict[Any, SampleBatch]], Optional["MultiAgentEpisode"]], SampleBatch]]): Optional callable for post-processing experience batches (called after the super's `postprocess_trajectory` method). stats_fn (Optional[Callable[[Policy, SampleBatch], Dict[str, TensorType]]]): Optional callable that returns a dict of values given the policy and training batch. If None, will use `TorchPolicy.extra_grad_info()` instead. The stats dict is used for logging (e.g. in TensorBoard). extra_action_out_fn (Optional[Callable[[Policy, Dict[str, TensorType], List[TensorType], ModelV2, TorchDistributionWrapper]], Dict[str, TensorType]]]): Optional callable that returns a dict of extra values to include in experiences. If None, no extra computations will be performed. extra_grad_process_fn (Optional[Callable[[Policy, "torch.optim.Optimizer", TensorType], Dict[str, TensorType]]]): Optional callable that is called after gradients are computed and returns a processing info dict. If None, will call the `TorchPolicy.extra_grad_process()` method instead. # TODO: (sven) dissolve naming mismatch between "learn" and "compute.." extra_learn_fetches_fn (Optional[Callable[[Policy], Dict[str, TensorType]]]): Optional callable that returns a dict of extra tensors from the policy after loss evaluation. If None, will call the `TorchPolicy.extra_compute_grad_fetches()` method instead. optimizer_fn (Optional[Callable[[Policy, TrainerConfigDict], "torch.optim.Optimizer"]]): Optional callable that returns a torch optimizer given the policy and config. If None, will call the `TorchPolicy.optimizer()` method instead (which returns a torch Adam optimizer). validate_spaces (Optional[Callable[[Policy, gym.Space, gym.Space, TrainerConfigDict], None]]): Optional callable that takes the Policy, observation_space, action_space, and config to check for correctness. If None, no spaces checking will be done. before_init (Optional[Callable[[Policy, gym.Space, gym.Space, TrainerConfigDict], None]]): Optional callable to run at the beginning of `Policy.__init__` that takes the same arguments as the Policy constructor. If None, this step will be skipped. before_loss_init (Optional[Callable[[Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict], None]]): Optional callable to run prior to loss init. If None, this step will be skipped. after_init (Optional[Callable[[Policy, gym.Space, gym.Space, TrainerConfigDict], None]]): DEPRECATED: Use `before_loss_init` instead. _after_loss_init (Optional[Callable[[Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict], None]]): Optional callable to run after the loss init. If None, this step will be skipped. This will be deprecated at some point and renamed into `after_init` to match `build_tf_policy()` behavior. action_sampler_fn (Optional[Callable[[TensorType, List[TensorType]], Tuple[TensorType, TensorType]]]): Optional callable returning a sampled action and its log-likelihood given some (obs and state) inputs. If None, will either use `action_distribution_fn` or compute actions by calling self.model, then sampling from the so parameterized action distribution. action_distribution_fn (Optional[Callable[[Policy, ModelV2, TensorType, TensorType, TensorType], Tuple[TensorType, Type[TorchDistributionWrapper], List[TensorType]]]]): A callable that takes the Policy, Model, the observation batch, an explore-flag, a timestep, and an is_training flag and returns a tuple of a) distribution inputs (parameters), b) a dist-class to generate an action distribution object from, and c) internal-state outputs (empty list if not applicable). If None, will either use `action_sampler_fn` or compute actions by calling self.model, then sampling from the parameterized action distribution. make_model (Optional[Callable[[Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict], ModelV2]]): Optional callable that takes the same arguments as Policy.__init__ and returns a model instance. The distribution class will be determined automatically. Note: Only one of `make_model` or `make_model_and_action_dist` should be provided. If both are None, a default Model will be created. make_model_and_action_dist (Optional[Callable[[Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict], Tuple[ModelV2, Type[TorchDistributionWrapper]]]]): Optional callable that takes the same arguments as Policy.__init__ and returns a tuple of model instance and torch action distribution class. Note: Only one of `make_model` or `make_model_and_action_dist` should be provided. If both are None, a default Model will be created. compute_gradients_fn (Optional[Callable[ [Policy, SampleBatch], Tuple[ModelGradients, dict]]]): Optional callable that the sampled batch an computes the gradients w.r. to the loss function. If None, will call the `TorchPolicy.compute_gradients()` method instead. apply_gradients_fn (Optional[Callable[[Policy, "torch.optim.Optimizer"], None]]): Optional callable that takes a grads list and applies these to the Model's parameters. If None, will call the `TorchPolicy.apply_gradients()` method instead. mixins (Optional[List[type]]): Optional list of any class mixins for the returned policy class. These mixins will be applied in order and will have higher precedence than the TorchPolicy class. get_batch_divisibility_req (Optional[Callable[[Policy], int]]): Optional callable that returns the divisibility requirement for sample batches. If None, will assume a value of 1. Returns: Type[TorchPolicy]: TorchPolicy child class constructed from the specified args. """ original_kwargs = locals().copy() parent_cls = TorchPolicy base = add_mixins(parent_cls, mixins) class policy_cls(base): def __init__(self, obs_space, action_space, config): # Set up the config from possible default-config fn and given # config arg. if get_default_config: config = dict(get_default_config(), **config) self.config = config # Set the DL framework for this Policy. self.framework = self.config["framework"] = framework # Validate observation- and action-spaces. if validate_spaces: validate_spaces(self, obs_space, action_space, self.config) # Do some pre-initialization steps. if before_init: before_init(self, obs_space, action_space, self.config) # Model is customized (use default action dist class). if make_model: assert make_model_and_action_dist is None, \ "Either `make_model` or `make_model_and_action_dist`" \ " must be None!" self.model = make_model(self, obs_space, action_space, config) dist_class, _ = ModelCatalog.get_action_dist( action_space, self.config["model"], framework=framework) # Model and action dist class are customized. elif make_model_and_action_dist: self.model, dist_class = make_model_and_action_dist( self, obs_space, action_space, config) # Use default model and default action dist. else: dist_class, logit_dim = ModelCatalog.get_action_dist( action_space, self.config["model"], framework=framework) self.model = ModelCatalog.get_model_v2( obs_space=obs_space, action_space=action_space, num_outputs=logit_dim, model_config=self.config["model"], framework=framework) # Make sure, we passed in a correct Model factory. model_cls = TorchModelV2 if framework == "torch" else JAXModelV2 assert isinstance(self.model, model_cls), \ "ERROR: Generated Model must be a TorchModelV2 object!" # Call the framework-specific Policy constructor. self.parent_cls = parent_cls self.parent_cls.__init__( self, observation_space=obs_space, action_space=action_space, config=config, model=self.model, loss=None if self.config["in_evaluation"] else loss_fn, action_distribution_class=dist_class, action_sampler_fn=action_sampler_fn, action_distribution_fn=action_distribution_fn, max_seq_len=config["model"]["max_seq_len"], get_batch_divisibility_req=get_batch_divisibility_req, ) # Merge Model's view requirements into Policy's. self.view_requirements.update(self.model.view_requirements) _before_loss_init = before_loss_init or after_init if _before_loss_init: _before_loss_init(self, self.observation_space, self.action_space, config) # Perform test runs through postprocessing- and loss functions. self._initialize_loss_from_dummy_batch( auto_remove_unneeded_view_reqs=True, stats_fn=None if self.config["in_evaluation"] else stats_fn, ) if _after_loss_init: _after_loss_init(self, obs_space, action_space, config) # Got to reset global_timestep again after this fake run-through. self.global_timestep = 0 @override(Policy) def postprocess_trajectory(self, sample_batch, other_agent_batches=None, episode=None): # Do all post-processing always with no_grad(). # Not using this here will introduce a memory leak # in torch (issue #6962). with self._no_grad_context(): # Call super's postprocess_trajectory first. sample_batch = super().postprocess_trajectory( sample_batch, other_agent_batches, episode) if postprocess_fn: return postprocess_fn(self, sample_batch, other_agent_batches, episode) return sample_batch @override(parent_cls) def extra_grad_process(self, optimizer, loss): """Called after optimizer.zero_grad() and loss.backward() calls. Allows for gradient processing before optimizer.step() is called. E.g. for gradient clipping. """ if extra_grad_process_fn: return extra_grad_process_fn(self, optimizer, loss) else: return parent_cls.extra_grad_process(self, optimizer, loss) @override(parent_cls) def extra_compute_grad_fetches(self): if extra_learn_fetches_fn: fetches = convert_to_non_torch_type( extra_learn_fetches_fn(self)) # Auto-add empty learner stats dict if needed. return dict({LEARNER_STATS_KEY: {}}, **fetches) else: return parent_cls.extra_compute_grad_fetches(self) @override(parent_cls) def compute_gradients(self, batch): if compute_gradients_fn: return compute_gradients_fn(self, batch) else: return parent_cls.compute_gradients(self, batch) @override(parent_cls) def apply_gradients(self, gradients): if apply_gradients_fn: apply_gradients_fn(self, gradients) else: parent_cls.apply_gradients(self, gradients) @override(parent_cls) def extra_action_out(self, input_dict, state_batches, model, action_dist): with self._no_grad_context(): if extra_action_out_fn: stats_dict = extra_action_out_fn( self, input_dict, state_batches, model, action_dist) else: stats_dict = parent_cls.extra_action_out( self, input_dict, state_batches, model, action_dist) return self._convert_to_non_torch_type(stats_dict) @override(parent_cls) def optimizer(self): if optimizer_fn: optimizers = optimizer_fn(self, self.config) else: optimizers = parent_cls.optimizer(self) optimizers = force_list(optimizers) if getattr(self, "exploration", None): optimizers = self.exploration.get_exploration_optimizer( optimizers) return optimizers @override(parent_cls) def extra_grad_info(self, train_batch): with self._no_grad_context(): if stats_fn: stats_dict = stats_fn(self, train_batch) else: stats_dict = self.parent_cls.extra_grad_info( self, train_batch) return self._convert_to_non_torch_type(stats_dict) def _no_grad_context(self): if self.framework == "torch": return torch.no_grad() return NullContextManager() def _convert_to_non_torch_type(self, data): if self.framework == "torch": return convert_to_non_torch_type(data) return data def with_updates(**overrides): """Creates a Torch|JAXPolicy cls based on settings of another one. Keyword Args: **overrides: The settings (passed into `build_torch_policy`) that should be different from the class that this method is called on. Returns: type: A new Torch|JAXPolicy sub-class. Examples: >> MySpecialDQNPolicyClass = DQNTorchPolicy.with_updates( .. name="MySpecialDQNPolicyClass", .. loss_function=[some_new_loss_function], .. ) """ return build_policy_class(**dict(original_kwargs, **overrides)) policy_cls.with_updates = staticmethod(with_updates) policy_cls.__name__ = name policy_cls.__qualname__ = name return policy_cls