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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import namedtuple
import numpy as np
import gym
from ray.rllib.utils.annotations import DeveloperAPI
# 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"
# Used to return tuple actions as a list of batches per tuple element
TupleActions = namedtuple("TupleActions", ["batches"])
@DeveloperAPI
class Policy(object):
"""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.
"""
@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
@DeveloperAPI
def compute_actions(self,
obs_batch,
state_batches,
prev_action_batch=None,
prev_reward_batch=None,
info_batch=None,
episodes=None,
**kwargs):
"""Compute actions for the current policy.
Arguments:
obs_batch (np.ndarray): batch of observations
state_batches (list): list of RNN state input batches, if any
prev_action_batch (np.ndarray): batch of previous action values
prev_reward_batch (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.
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,
prev_action=None,
prev_reward=None,
info=None,
episode=None,
clip_actions=False,
**kwargs):
"""Unbatched version of compute_actions.
Arguments:
obs (obj): single observation
state_batches (list): list of RNN state inputs, if any
prev_action (obj): previous action value, if any
prev_reward (int): 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 the action be clipped
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
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]
[action], state_out, info = self.compute_actions(
[obs], [[s] for s in state],
prev_action_batch=prev_action_batch,
prev_reward_batch=prev_reward_batch,
info_batch=info_batch,
episodes=episodes)
if clip_actions:
action = clip_action(action, self.action_space)
return action, [s[0] for s in state_out], \
{k: v[0] for k, v in info.items()}
@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_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.
"""
pass
@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
def clip_action(action, space):
"""Called to clip actions to the specified range of this policy.
Arguments:
action: Single action.
space: Action space the actions should be present in.
Returns:
Clipped batch of actions.
"""
if isinstance(space, gym.spaces.Box):
return np.clip(action, space.low, space.high)
elif isinstance(space, gym.spaces.Tuple):
if type(action) not in (tuple, list):
raise ValueError("Expected tuple space for actions {}: {}".format(
action, space))
out = []
for a, s in zip(action, space.spaces):
out.append(clip_action(a, s))
return out
else:
return action
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