Source code for ray.rllib.policy.policy

from abc import ABCMeta, abstractmethod
import gym
import numpy as np
from typing import Any

from ray.rllib.utils import try_import_tree
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.utils.exploration.exploration import Exploration
from ray.rllib.utils.from_config import from_config
from ray.rllib.utils.spaces.space_utils import get_base_struct_from_space, \
    unbatch

tree = try_import_tree()

# By convention, metrics from optimizing the loss can be reported in the
# `grad_info` dict returned by learn_on_batch() / compute_grads() via this key.
LEARNER_STATS_KEY = "learner_stats"

# Represents a generic identifier for an agent (e.g., "agent1").
AgentID = Any

# Represents a generic identifier for a policy (e.g., "pol1").
PolicyID = str


[docs]@DeveloperAPI class Policy(metaclass=ABCMeta): """An agent policy and loss, i.e., a TFPolicy or other subclass. This object defines how to act in the environment, and also losses used to improve the policy based on its experiences. Note that both policy and loss are defined together for convenience, though the policy itself is logically separate. All policies can directly extend Policy, however TensorFlow users may find TFPolicy simpler to implement. TFPolicy also enables RLlib to apply TensorFlow-specific optimizations such as fusing multiple policy graphs and multi-GPU support. Attributes: observation_space (gym.Space): Observation space of the policy. action_space (gym.Space): Action space of the policy. exploration (Exploration): The exploration object to use for computing actions, or None. """ @DeveloperAPI def __init__(self, observation_space, action_space, config): """Initialize the graph. This is the standard constructor for policies. The policy class you pass into RolloutWorker will be constructed with these arguments. Args: observation_space (gym.Space): Observation space of the policy. action_space (gym.Space): Action space of the policy. config (dict): Policy-specific configuration data. """ self.observation_space = observation_space self.action_space = action_space self.action_space_struct = get_base_struct_from_space(action_space) self.config = config # The global timestep, broadcast down from time to time from the # driver. self.global_timestep = 0 # The action distribution class to use for action sampling, if any. # Child classes may set this. self.dist_class = None
[docs] @abstractmethod @DeveloperAPI def compute_actions(self, obs_batch, state_batches=None, prev_action_batch=None, prev_reward_batch=None, info_batch=None, episodes=None, explore=None, timestep=None, **kwargs): """Computes actions for the current policy. Args: obs_batch (Union[List,np.ndarray]): Batch of observations. state_batches (Optional[list]): List of RNN state input batches, if any. prev_action_batch (Optional[List,np.ndarray]): Batch of previous action values. prev_reward_batch (Optional[List,np.ndarray]): Batch of previous rewards. info_batch (info): Batch of info objects. episodes (list): MultiAgentEpisode for each obs in obs_batch. This provides access to all of the internal episode state, which may be useful for model-based or multiagent algorithms. explore (bool): Whether to pick an exploitation or exploration action (default: None -> use self.config["explore"]). timestep (int): The current (sampling) time step. kwargs: forward compatibility placeholder Returns: actions (np.ndarray): batch of output actions, with shape like [BATCH_SIZE, ACTION_SHAPE]. state_outs (list): list of RNN state output batches, if any, with shape like [STATE_SIZE, BATCH_SIZE]. info (dict): dictionary of extra feature batches, if any, with shape like {"f1": [BATCH_SIZE, ...], "f2": [BATCH_SIZE, ...]}. """ raise NotImplementedError
[docs] @DeveloperAPI def compute_single_action(self, obs, state=None, prev_action=None, prev_reward=None, info=None, episode=None, clip_actions=False, explore=None, timestep=None, **kwargs): """Unbatched version of compute_actions. Arguments: obs (obj): Single observation. state (list): List of RNN state inputs, if any. prev_action (obj): Previous action value, if any. prev_reward (float): Previous reward, if any. info (dict): info object, if any episode (MultiAgentEpisode): this provides access to all of the internal episode state, which may be useful for model-based or multi-agent algorithms. clip_actions (bool): Should actions be clipped? explore (bool): Whether to pick an exploitation or exploration action (default: None -> use self.config["explore"]). timestep (int): The current (sampling) time step. kwargs: forward compatibility placeholder Returns: actions (obj): single action state_outs (list): list of RNN state outputs, if any info (dict): dictionary of extra features, if any """ prev_action_batch = None prev_reward_batch = None info_batch = None episodes = None state_batch = None if prev_action is not None: prev_action_batch = [prev_action] if prev_reward is not None: prev_reward_batch = [prev_reward] if info is not None: info_batch = [info] if episode is not None: episodes = [episode] if state is not None: state_batch = [[s] for s in state] batched_action, state_out, info = self.compute_actions( [obs], state_batch, prev_action_batch=prev_action_batch, prev_reward_batch=prev_reward_batch, info_batch=info_batch, episodes=episodes, explore=explore, timestep=timestep) single_action = unbatch(batched_action) assert len(single_action) == 1 single_action = single_action[0] if clip_actions: single_action = clip_action(single_action, self.action_space_struct) # Return action, internal state(s), infos. return single_action, [s[0] for s in state_out], \ {k: v[0] for k, v in info.items()}
[docs] @DeveloperAPI def compute_log_likelihoods(self, actions, obs_batch, state_batches=None, prev_action_batch=None, prev_reward_batch=None): """Computes the log-prob/likelihood for a given action and observation. Args: actions (Union[List,np.ndarray]): Batch of actions, for which to retrieve the log-probs/likelihoods (given all other inputs: obs, states, ..). obs_batch (Union[List,np.ndarray]): Batch of observations. state_batches (Optional[list]): List of RNN state input batches, if any. prev_action_batch (Optional[List,np.ndarray]): Batch of previous action values. prev_reward_batch (Optional[List,np.ndarray]): Batch of previous rewards. Returns: log-likelihoods (np.ndarray): Batch of log probs/likelihoods, with shape: [BATCH_SIZE]. """ raise NotImplementedError
[docs] @DeveloperAPI def postprocess_trajectory(self, sample_batch, other_agent_batches=None, episode=None): """Implements algorithm-specific trajectory postprocessing. This will be called on each trajectory fragment computed during policy evaluation. Each fragment is guaranteed to be only from one episode. Arguments: sample_batch (SampleBatch): batch of experiences for the policy, which will contain at most one episode trajectory. other_agent_batches (dict): In a multi-agent env, this contains a mapping of agent ids to (policy, agent_batch) tuples containing the policy and experiences of the other agents. episode (MultiAgentEpisode): this provides access to all of the internal episode state, which may be useful for model-based or multi-agent algorithms. Returns: SampleBatch: Postprocessed sample batch. """ return sample_batch
[docs] @DeveloperAPI def learn_on_batch(self, samples): """Fused compute gradients and apply gradients call. Either this or the combination of compute/apply grads must be implemented by subclasses. Returns: grad_info: dictionary of extra metadata from compute_gradients(). Examples: >>> batch = ev.sample() >>> ev.learn_on_batch(samples) """ grads, grad_info = self.compute_gradients(samples) self.apply_gradients(grads) return grad_info
[docs] @DeveloperAPI def compute_gradients(self, postprocessed_batch): """Computes gradients against a batch of experiences. Either this or learn_on_batch() must be implemented by subclasses. Returns: grads (list): List of gradient output values info (dict): Extra policy-specific values """ raise NotImplementedError
[docs] @DeveloperAPI def apply_gradients(self, gradients): """Applies previously computed gradients. Either this or learn_on_batch() must be implemented by subclasses. """ raise NotImplementedError
[docs] @DeveloperAPI def get_weights(self): """Returns model weights. Returns: weights (obj): Serializable copy or view of model weights """ raise NotImplementedError
[docs] @DeveloperAPI def set_weights(self, weights): """Sets model weights. Arguments: weights (obj): Serializable copy or view of model weights """ raise NotImplementedError
[docs] @DeveloperAPI def get_exploration_info(self): """Returns the current exploration information of this policy. This information depends on the policy's Exploration object. Returns: any: Serializable information on the `self.exploration` object. """ return self.exploration.get_info()
[docs] @DeveloperAPI def is_recurrent(self): """Whether this Policy holds a recurrent Model. Returns: bool: True if this Policy has-a RNN-based Model. """ return 0
[docs] @DeveloperAPI def num_state_tensors(self): """The number of internal states needed by the RNN-Model of the Policy. Returns: int: The number of RNN internal states kept by this Policy's Model. """ return 0
[docs] @DeveloperAPI def get_initial_state(self): """Returns initial RNN state for the current policy.""" return []
[docs] @DeveloperAPI def get_state(self): """Saves all local state. Returns: state (obj): Serialized local state. """ return self.get_weights()
[docs] @DeveloperAPI def set_state(self, state): """Restores all local state. Arguments: state (obj): Serialized local state. """ self.set_weights(state)
[docs] @DeveloperAPI def on_global_var_update(self, global_vars): """Called on an update to global vars. Arguments: global_vars (dict): Global variables broadcast from the driver. """ # Store the current global time step (sum over all policies' sample # steps). self.global_timestep = global_vars["timestep"]
[docs] @DeveloperAPI def export_model(self, export_dir): """Export Policy to local directory for serving. Arguments: export_dir (str): Local writable directory. """ raise NotImplementedError
[docs] @DeveloperAPI def export_checkpoint(self, export_dir): """Export Policy checkpoint to local directory. Argument: export_dir (str): Local writable directory. """ raise NotImplementedError
[docs] @DeveloperAPI def import_model_from_h5(self, import_file): """Imports Policy from local file. Arguments: import_file (str): Local readable file. """ raise NotImplementedError
def _create_exploration(self): """Creates the Policy's Exploration object. This method only exists b/c some Trainers do not use TfPolicy nor TorchPolicy, but inherit directly from Policy. Others inherit from TfPolicy w/o using DynamicTfPolicy. TODO(sven): unify these cases.""" if getattr(self, "exploration", None) is not None: return self.exploration exploration = from_config( Exploration, self.config.get("exploration_config", {"type": "StochasticSampling"}), action_space=self.action_space, policy_config=self.config, model=getattr(self, "model", None), num_workers=self.config.get("num_workers", 0), worker_index=self.config.get("worker_index", 0), framework=getattr(self, "framework", "tf")) return exploration
def clip_action(action, action_space): """Clips all actions in `flat_actions` according to the given Spaces. Args: flat_actions (List[np.ndarray]): The (flattened) list of single action components. List will have len=1 for "primitive" action Spaces. flat_space (List[Space]): The (flattened) list of single action Space objects. Has to be of same length as `flat_actions`. Returns: List[np.ndarray]: Flattened list of single clipped "primitive" actions. """ def map_(a, s): if isinstance(s, gym.spaces.Box): a = np.clip(a, s.low, s.high) return a return tree.map_structure(map_, action, action_space)