ray/rllib/policy/policy.py

from abc import ABCMeta, abstractmethod
import gym
import numpy as np
from typing import Dict, List, Optional

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.framework import try_import_torch
from ray.rllib.utils.from_config import from_config
from ray.rllib.utils.spaces.space_utils import get_base_struct_from_space, \
    unbatch
from ray.rllib.utils.types import AgentID

torch, _ = try_import_torch()
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"


@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

    @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

    @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.unsqueeze(0) if torch and isinstance(s, torch.Tensor) else
                np.expand_dims(s, 0)
                for s in state
            ]

        out = 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)

        # Some policies don't return a tuple, but always just a single action.
        # E.g. ES and ARS.
        if not isinstance(out, tuple):
            single_action = out
            state_out = []
            info = {}
        # Normal case: Policy should return (action, state, info) tuple.
        else:
            batched_action, state_out, info = out
            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()}

    def compute_actions_from_trajectories(
            self,
            trajectories: List["Trajectory"],
            other_trajectories: Dict[AgentID, "Trajectory"],
            explore: bool = None,
            timestep: Optional[int] = None,
            **kwargs):
        """Computes actions for the current policy based on .

        Note: This is an experimental API method.

        Only used so far by the Sampler iff `_fast_sampling=True` (also only
        supported for torch).

        Args:
            trajectories (List[Trajectory]): A List of Trajectory data used
                to create a view for the Model forward call.
            other_trajectories (Dict[AgentID, Trajectory]): Optional dict
                mapping AgentIDs to Trajectory objects.
            explore (bool): Whether to pick an exploitation or exploration
                action (default: None -> use self.config["explore"]).
            timestep (Optional[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

    @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

    @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

    @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

    @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

    @DeveloperAPI
    def apply_gradients(self, gradients):
        """Applies previously computed gradients.

        Either this or learn_on_batch() must be implemented by subclasses.
        """
        raise NotImplementedError

    @DeveloperAPI
    def get_weights(self):
        """Returns model weights.

        Returns:
            weights (obj): Serializable copy or view of model weights
        """
        raise NotImplementedError

    @DeveloperAPI
    def set_weights(self, weights):
        """Sets model weights.

        Arguments:
            weights (obj): Serializable copy or view of model weights
        """
        raise NotImplementedError

    @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()

    @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

    @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

    @DeveloperAPI
    def get_initial_state(self):
        """Returns initial RNN state for the current policy."""
        return []

    @DeveloperAPI
    def get_state(self):
        """Saves all local state.

        Returns:
            state (obj): Serialized local state.
        """
        return self.get_weights()

    @DeveloperAPI
    def set_state(self, state):
        """Restores all local state.

        Arguments:
            state (obj): Serialized local state.
        """
        self.set_weights(state)

    @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"]

    @DeveloperAPI
    def export_model(self, export_dir):
        """Export Policy to local directory for serving.

        Arguments:
            export_dir (str): Local writable directory.
        """
        raise NotImplementedError

    @DeveloperAPI
    def export_checkpoint(self, export_dir):
        """Export Policy checkpoint to local directory.

        Argument:
            export_dir (str): Local writable directory.
        """
        raise NotImplementedError

    @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)