mirror of
https://github.com/wassname/ray.git
synced 2026-07-01 18:57:37 +08:00
Eric Liang
643 lines
27 KiB
Python
643 lines
27 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import gym
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import logging
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import pickle
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import tensorflow as tf
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import ray
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from ray.rllib.env.async_vector_env import AsyncVectorEnv
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from ray.rllib.env.atari_wrappers import wrap_deepmind, is_atari
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from ray.rllib.env.env_context import EnvContext
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from ray.rllib.env.multi_agent_env import MultiAgentEnv
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from ray.rllib.evaluation.interface import EvaluatorInterface
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from ray.rllib.evaluation.sample_batch import MultiAgentBatch, \
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DEFAULT_POLICY_ID
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from ray.rllib.evaluation.sampler import AsyncSampler, SyncSampler
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from ray.rllib.evaluation.policy_graph import PolicyGraph
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from ray.rllib.evaluation.tf_policy_graph import TFPolicyGraph
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from ray.rllib.offline import NoopOutput, IOContext, OutputWriter, InputReader
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from ray.rllib.models import ModelCatalog
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from ray.rllib.models.preprocessors import NoPreprocessor
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from ray.rllib.utils import merge_dicts
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils.compression import pack
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from ray.rllib.utils.filter import get_filter
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from ray.rllib.utils.tf_run_builder import TFRunBuilder
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logger = logging.getLogger(__name__)
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class PolicyEvaluator(EvaluatorInterface):
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"""Common ``PolicyEvaluator`` implementation that wraps a ``PolicyGraph``.
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This class wraps a policy graph instance and an environment class to
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collect experiences from the environment. You can create many replicas of
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this class as Ray actors to scale RL training.
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This class supports vectorized and multi-agent policy evaluation (e.g.,
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VectorEnv, MultiAgentEnv, etc.)
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Examples:
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>>> # Create a policy evaluator and using it to collect experiences.
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>>> evaluator = PolicyEvaluator(
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... env_creator=lambda _: gym.make("CartPole-v0"),
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... policy_graph=PGPolicyGraph)
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>>> print(evaluator.sample())
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SampleBatch({
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"obs": [[...]], "actions": [[...]], "rewards": [[...]],
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"dones": [[...]], "new_obs": [[...]]})
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>>> # Creating policy evaluators using optimizer_cls.make().
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>>> optimizer = SyncSamplesOptimizer.make(
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... evaluator_cls=PolicyEvaluator,
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... evaluator_args={
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... "env_creator": lambda _: gym.make("CartPole-v0"),
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... "policy_graph": PGPolicyGraph,
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... },
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... num_workers=10)
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>>> for _ in range(10): optimizer.step()
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>>> # Creating a multi-agent policy evaluator
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>>> evaluator = PolicyEvaluator(
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... env_creator=lambda _: MultiAgentTrafficGrid(num_cars=25),
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... policy_graphs={
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... # Use an ensemble of two policies for car agents
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... "car_policy1":
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... (PGPolicyGraph, Box(...), Discrete(...), {"gamma": 0.99}),
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... "car_policy2":
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... (PGPolicyGraph, Box(...), Discrete(...), {"gamma": 0.95}),
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... # Use a single shared policy for all traffic lights
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... "traffic_light_policy":
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... (PGPolicyGraph, Box(...), Discrete(...), {}),
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... },
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... policy_mapping_fn=lambda agent_id:
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... random.choice(["car_policy1", "car_policy2"])
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... if agent_id.startswith("car_") else "traffic_light_policy")
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>>> print(evaluator.sample())
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MultiAgentBatch({
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"car_policy1": SampleBatch(...),
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"car_policy2": SampleBatch(...),
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"traffic_light_policy": SampleBatch(...)})
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"""
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@classmethod
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def as_remote(cls, num_cpus=None, num_gpus=None, resources=None):
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return ray.remote(
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num_cpus=num_cpus, num_gpus=num_gpus, resources=resources)(cls)
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def __init__(self,
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env_creator,
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policy_graph,
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policy_mapping_fn=None,
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policies_to_train=None,
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tf_session_creator=None,
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batch_steps=100,
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batch_mode="truncate_episodes",
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episode_horizon=None,
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preprocessor_pref="deepmind",
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sample_async=False,
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compress_observations=False,
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num_envs=1,
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observation_filter="NoFilter",
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clip_rewards=None,
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clip_actions=True,
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env_config=None,
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model_config=None,
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policy_config=None,
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worker_index=0,
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monitor_path=None,
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log_dir=None,
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log_level=None,
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callbacks=None,
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input_creator=lambda ioctx: ioctx.default_sampler_input(),
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input_evaluation_method=None,
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output_creator=lambda ioctx: NoopOutput()):
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"""Initialize a policy evaluator.
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Arguments:
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env_creator (func): Function that returns a gym.Env given an
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EnvContext wrapped configuration.
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policy_graph (class|dict): Either a class implementing
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PolicyGraph, or a dictionary of policy id strings to
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(PolicyGraph, obs_space, action_space, config) tuples. If a
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dict is specified, then we are in multi-agent mode and a
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policy_mapping_fn should also be set.
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policy_mapping_fn (func): A function that maps agent ids to
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policy ids in multi-agent mode. This function will be called
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each time a new agent appears in an episode, to bind that agent
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to a policy for the duration of the episode.
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policies_to_train (list): Optional whitelist of policies to train,
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or None for all policies.
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tf_session_creator (func): A function that returns a TF session.
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This is optional and only useful with TFPolicyGraph.
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batch_steps (int): The target number of env transitions to include
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in each sample batch returned from this evaluator.
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batch_mode (str): One of the following batch modes:
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"truncate_episodes": Each call to sample() will return a batch
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of at most `batch_steps * num_envs` in size. The batch will
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be exactly `batch_steps * num_envs` in size if
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postprocessing does not change batch sizes. Episodes may be
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truncated in order to meet this size requirement.
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"complete_episodes": Each call to sample() will return a batch
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of at least `batch_steps * num_envs` in size. Episodes will
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not be truncated, but multiple episodes may be packed
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within one batch to meet the batch size. Note that when
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`num_envs > 1`, episode steps will be buffered until the
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episode completes, and hence batches may contain
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significant amounts of off-policy data.
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episode_horizon (int): Whether to stop episodes at this horizon.
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preprocessor_pref (str): Whether to prefer RLlib preprocessors
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("rllib") or deepmind ("deepmind") when applicable.
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sample_async (bool): Whether to compute samples asynchronously in
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the background, which improves throughput but can cause samples
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to be slightly off-policy.
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compress_observations (bool): If true, compress the observations.
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They can be decompressed with rllib/utils/compression.
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num_envs (int): If more than one, will create multiple envs
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and vectorize the computation of actions. This has no effect if
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if the env already implements VectorEnv.
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observation_filter (str): Name of observation filter to use.
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clip_rewards (bool): Whether to clip rewards to [-1, 1] prior to
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experience postprocessing. Setting to None means clip for Atari
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only.
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clip_actions (bool): Whether to clip action values to the range
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specified by the policy action space.
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env_config (dict): Config to pass to the env creator.
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model_config (dict): Config to use when creating the policy model.
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policy_config (dict): Config to pass to the policy. In the
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multi-agent case, this config will be merged with the
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per-policy configs specified by `policy_graph`.
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worker_index (int): For remote evaluators, this should be set to a
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non-zero and unique value. This index is passed to created envs
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through EnvContext so that envs can be configured per worker.
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monitor_path (str): Write out episode stats and videos to this
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directory if specified.
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log_dir (str): Directory where logs can be placed.
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log_level (str): Set the root log level on creation.
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callbacks (dict): Dict of custom debug callbacks.
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input_creator (func): Function that returns an InputReader object
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for loading previous generated experiences.
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input_evaluation_method (str): How to evaluate the current policy.
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This only applies when the input is reading offline data.
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Options are:
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- None: don't evaluate the policy. The episode reward and
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other metrics will be NaN.
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- "simulation": run the environment in the background, but
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use this data for evaluation only and never for learning.
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output_creator (func): Function that returns an OutputWriter object
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for saving generated experiences.
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"""
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if log_level:
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logging.getLogger("ray.rllib").setLevel(log_level)
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env_context = EnvContext(env_config or {}, worker_index)
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policy_config = policy_config or {}
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self.policy_config = policy_config
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self.callbacks = callbacks or {}
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model_config = model_config or {}
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policy_mapping_fn = (policy_mapping_fn
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or (lambda agent_id: DEFAULT_POLICY_ID))
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if not callable(policy_mapping_fn):
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raise ValueError(
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"Policy mapping function not callable. If you're using Tune, "
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"make sure to escape the function with tune.function() "
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"to prevent it from being evaluated as an expression.")
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self.env_creator = env_creator
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self.sample_batch_size = batch_steps * num_envs
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self.batch_mode = batch_mode
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self.compress_observations = compress_observations
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self.preprocessing_enabled = True
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self.env = env_creator(env_context)
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if isinstance(self.env, MultiAgentEnv) or \
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isinstance(self.env, AsyncVectorEnv):
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def wrap(env):
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return env # we can't auto-wrap these env types
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elif is_atari(self.env) and \
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not model_config.get("custom_preprocessor") and \
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preprocessor_pref == "deepmind":
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# Deepmind wrappers already handle all preprocessing
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self.preprocessing_enabled = False
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if clip_rewards is None:
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clip_rewards = True
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def wrap(env):
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env = wrap_deepmind(
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env,
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dim=model_config.get("dim"),
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framestack=model_config.get("framestack"))
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if monitor_path:
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env = _monitor(env, monitor_path)
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return env
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else:
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def wrap(env):
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if monitor_path:
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env = _monitor(env, monitor_path)
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return env
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self.env = wrap(self.env)
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def make_env(vector_index):
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return wrap(
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env_creator(env_context.with_vector_index(vector_index)))
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self.tf_sess = None
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policy_dict = _validate_and_canonicalize(policy_graph, self.env)
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self.policies_to_train = policies_to_train or list(policy_dict.keys())
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if _has_tensorflow_graph(policy_dict):
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if (ray.worker._mode() != ray.worker.LOCAL_MODE
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and not ray.get_gpu_ids()):
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logger.info("Creating policy evaluation worker {}".format(
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worker_index) +
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" on CPU (please ignore any CUDA init errors)")
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with tf.Graph().as_default():
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if tf_session_creator:
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self.tf_sess = tf_session_creator()
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else:
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self.tf_sess = tf.Session(
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config=tf.ConfigProto(
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gpu_options=tf.GPUOptions(allow_growth=True)))
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with self.tf_sess.as_default():
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self.policy_map, self.preprocessors = \
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self._build_policy_map(policy_dict, policy_config)
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else:
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self.policy_map, self.preprocessors = self._build_policy_map(
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policy_dict, policy_config)
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self.multiagent = set(self.policy_map.keys()) != {DEFAULT_POLICY_ID}
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if self.multiagent:
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if not (isinstance(self.env, MultiAgentEnv)
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or isinstance(self.env, AsyncVectorEnv)):
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raise ValueError(
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"Have multiple policy graphs {}, but the env ".format(
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self.policy_map) +
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"{} is not a subclass of MultiAgentEnv?".format(self.env))
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self.filters = {
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policy_id: get_filter(observation_filter,
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policy.observation_space.shape)
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for (policy_id, policy) in self.policy_map.items()
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}
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# Always use vector env for consistency even if num_envs = 1
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self.async_env = AsyncVectorEnv.wrap_async(
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self.env, make_env=make_env, num_envs=num_envs)
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self.num_envs = num_envs
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if self.batch_mode == "truncate_episodes":
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unroll_length = batch_steps
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pack_episodes = True
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elif self.batch_mode == "complete_episodes":
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unroll_length = float("inf") # never cut episodes
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pack_episodes = False # sampler will return 1 episode per poll
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else:
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raise ValueError("Unsupported batch mode: {}".format(
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self.batch_mode))
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if input_evaluation_method == "simulation":
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logger.warning(
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"Requested 'simulation' input evaluation method: "
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"will discard all sampler outputs and keep only metrics.")
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sample_async = True
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elif input_evaluation_method is None:
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pass
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else:
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raise ValueError("Unknown evaluation method: {}".format(
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input_evaluation_method))
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if sample_async:
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self.sampler = AsyncSampler(
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self.async_env,
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self.policy_map,
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policy_mapping_fn,
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self.preprocessors,
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self.filters,
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clip_rewards,
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unroll_length,
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self.callbacks,
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horizon=episode_horizon,
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pack=pack_episodes,
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tf_sess=self.tf_sess,
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clip_actions=clip_actions,
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blackhole_outputs=input_evaluation_method == "simulation")
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self.sampler.start()
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else:
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self.sampler = SyncSampler(
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self.async_env,
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self.policy_map,
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policy_mapping_fn,
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self.preprocessors,
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self.filters,
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clip_rewards,
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unroll_length,
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self.callbacks,
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horizon=episode_horizon,
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pack=pack_episodes,
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tf_sess=self.tf_sess,
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clip_actions=clip_actions)
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self.io_context = IOContext(log_dir, policy_config, worker_index, self)
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self.input_reader = input_creator(self.io_context)
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assert isinstance(self.input_reader, InputReader), self.input_reader
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self.output_writer = output_creator(self.io_context)
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assert isinstance(self.output_writer, OutputWriter), self.output_writer
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logger.debug("Created evaluator with env {} ({}), policies {}".format(
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self.async_env, self.env, self.policy_map))
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@override(EvaluatorInterface)
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def sample(self):
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"""Evaluate the current policies and return a batch of experiences.
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Return:
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SampleBatch|MultiAgentBatch from evaluating the current policies.
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"""
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batches = [self.input_reader.next()]
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steps_so_far = batches[0].count
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# In truncate_episodes mode, never pull more than 1 batch per env.
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# This avoids over-running the target batch size.
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if self.batch_mode == "truncate_episodes":
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max_batches = self.num_envs
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else:
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max_batches = float("inf")
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while steps_so_far < self.sample_batch_size and len(
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batches) < max_batches:
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batch = self.input_reader.next()
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steps_so_far += batch.count
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batches.append(batch)
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batch = batches[0].concat_samples(batches)
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if self.callbacks.get("on_sample_end"):
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self.callbacks["on_sample_end"]({
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"evaluator": self,
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"samples": batch
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})
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# Always do writes prior to compression for consistency and to allow
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# for better compression inside the writer.
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self.output_writer.write(batch)
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if self.compress_observations:
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if isinstance(batch, MultiAgentBatch):
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for data in batch.policy_batches.values():
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data["obs"] = [pack(o) for o in data["obs"]]
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data["new_obs"] = [pack(o) for o in data["new_obs"]]
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else:
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batch["obs"] = [pack(o) for o in batch["obs"]]
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batch["new_obs"] = [pack(o) for o in batch["new_obs"]]
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return batch
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@ray.method(num_return_vals=2)
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def sample_with_count(self):
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"""Same as sample() but returns the count as a separate future."""
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batch = self.sample()
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return batch, batch.count
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@override(EvaluatorInterface)
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def get_weights(self, policies=None):
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if policies is None:
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policies = self.policy_map.keys()
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return {
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pid: policy.get_weights()
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for pid, policy in self.policy_map.items() if pid in policies
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}
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@override(EvaluatorInterface)
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def set_weights(self, weights):
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for pid, w in weights.items():
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self.policy_map[pid].set_weights(w)
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@override(EvaluatorInterface)
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def compute_gradients(self, samples):
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if isinstance(samples, MultiAgentBatch):
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grad_out, info_out = {}, {}
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if self.tf_sess is not None:
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builder = TFRunBuilder(self.tf_sess, "compute_gradients")
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for pid, batch in samples.policy_batches.items():
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if pid not in self.policies_to_train:
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continue
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grad_out[pid], info_out[pid] = (
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self.policy_map[pid]._build_compute_gradients(
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builder, batch))
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grad_out = {k: builder.get(v) for k, v in grad_out.items()}
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info_out = {k: builder.get(v) for k, v in info_out.items()}
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else:
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for pid, batch in samples.policy_batches.items():
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if pid not in self.policies_to_train:
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continue
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grad_out[pid], info_out[pid] = (
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self.policy_map[pid].compute_gradients(batch))
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else:
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grad_out, info_out = (
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self.policy_map[DEFAULT_POLICY_ID].compute_gradients(samples))
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info_out["batch_count"] = samples.count
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return grad_out, info_out
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@override(EvaluatorInterface)
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def apply_gradients(self, grads):
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if isinstance(grads, dict):
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if self.tf_sess is not None:
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builder = TFRunBuilder(self.tf_sess, "apply_gradients")
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outputs = {
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pid: self.policy_map[pid]._build_apply_gradients(
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builder, grad)
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for pid, grad in grads.items()
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}
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return {k: builder.get(v) for k, v in outputs.items()}
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else:
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return {
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pid: self.policy_map[pid].apply_gradients(g)
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for pid, g in grads.items()
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}
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else:
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return self.policy_map[DEFAULT_POLICY_ID].apply_gradients(grads)
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@override(EvaluatorInterface)
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def compute_apply(self, samples):
|
|
if isinstance(samples, MultiAgentBatch):
|
|
info_out = {}
|
|
if self.tf_sess is not None:
|
|
builder = TFRunBuilder(self.tf_sess, "compute_apply")
|
|
for pid, batch in samples.policy_batches.items():
|
|
if pid not in self.policies_to_train:
|
|
continue
|
|
info_out[pid], _ = (
|
|
self.policy_map[pid]._build_compute_apply(
|
|
builder, batch))
|
|
info_out = {k: builder.get(v) for k, v in info_out.items()}
|
|
else:
|
|
for pid, batch in samples.policy_batches.items():
|
|
if pid not in self.policies_to_train:
|
|
continue
|
|
info_out[pid], _ = (
|
|
self.policy_map[pid].compute_apply(batch))
|
|
return info_out
|
|
else:
|
|
grad_fetch, apply_fetch = (
|
|
self.policy_map[DEFAULT_POLICY_ID].compute_apply(samples))
|
|
return grad_fetch
|
|
|
|
def for_policy(self, func, policy_id=DEFAULT_POLICY_ID):
|
|
"""Apply the given function to the specified policy graph."""
|
|
|
|
return func(self.policy_map[policy_id])
|
|
|
|
def foreach_policy(self, func):
|
|
"""Apply the given function to each (policy, policy_id) tuple."""
|
|
|
|
return [func(policy, pid) for pid, policy in self.policy_map.items()]
|
|
|
|
def foreach_trainable_policy(self, func):
|
|
"""Apply the given function to each (policy, policy_id) tuple.
|
|
|
|
This only applies func to policies in `self.policies_to_train`."""
|
|
|
|
return [
|
|
func(policy, pid) for pid, policy in self.policy_map.items()
|
|
if pid in self.policies_to_train
|
|
]
|
|
|
|
def sync_filters(self, new_filters):
|
|
"""Changes self's filter to given and rebases any accumulated delta.
|
|
|
|
Args:
|
|
new_filters (dict): Filters with new state to update local copy.
|
|
"""
|
|
assert all(k in new_filters for k in self.filters)
|
|
for k in self.filters:
|
|
self.filters[k].sync(new_filters[k])
|
|
|
|
def get_filters(self, flush_after=False):
|
|
"""Returns a snapshot of filters.
|
|
|
|
Args:
|
|
flush_after (bool): Clears the filter buffer state.
|
|
|
|
Returns:
|
|
return_filters (dict): Dict for serializable filters
|
|
"""
|
|
return_filters = {}
|
|
for k, f in self.filters.items():
|
|
return_filters[k] = f.as_serializable()
|
|
if flush_after:
|
|
f.clear_buffer()
|
|
return return_filters
|
|
|
|
def save(self):
|
|
filters = self.get_filters(flush_after=True)
|
|
state = {
|
|
pid: self.policy_map[pid].get_state()
|
|
for pid in self.policy_map
|
|
}
|
|
return pickle.dumps({"filters": filters, "state": state})
|
|
|
|
def restore(self, objs):
|
|
objs = pickle.loads(objs)
|
|
self.sync_filters(objs["filters"])
|
|
for pid, state in objs["state"].items():
|
|
self.policy_map[pid].set_state(state)
|
|
|
|
def set_global_vars(self, global_vars):
|
|
self.foreach_policy(lambda p, _: p.on_global_var_update(global_vars))
|
|
|
|
def export_policy_model(self, export_dir, policy_id=DEFAULT_POLICY_ID):
|
|
self.policy_map[policy_id].export_model(export_dir)
|
|
|
|
def export_policy_checkpoint(self,
|
|
export_dir,
|
|
filename_prefix="model",
|
|
policy_id=DEFAULT_POLICY_ID):
|
|
self.policy_map[policy_id].export_checkpoint(export_dir,
|
|
filename_prefix)
|
|
|
|
def _build_policy_map(self, policy_dict, policy_config):
|
|
policy_map = {}
|
|
preprocessors = {}
|
|
for name, (cls, obs_space, act_space,
|
|
conf) in sorted(policy_dict.items()):
|
|
merged_conf = merge_dicts(policy_config, conf)
|
|
if self.preprocessing_enabled:
|
|
preprocessor = ModelCatalog.get_preprocessor_for_space(
|
|
obs_space, merged_conf.get("model"))
|
|
preprocessors[name] = preprocessor
|
|
obs_space = preprocessor.observation_space
|
|
else:
|
|
preprocessors[name] = NoPreprocessor(obs_space)
|
|
if isinstance(obs_space, gym.spaces.Dict) or \
|
|
isinstance(obs_space, gym.spaces.Tuple):
|
|
raise ValueError(
|
|
"Found raw Tuple|Dict space as input to policy graph. "
|
|
"Please preprocess these observations with a "
|
|
"Tuple|DictFlatteningPreprocessor.")
|
|
with tf.variable_scope(name):
|
|
policy_map[name] = cls(obs_space, act_space, merged_conf)
|
|
return policy_map, preprocessors
|
|
|
|
def __del__(self):
|
|
if isinstance(self.sampler, AsyncSampler):
|
|
self.sampler.shutdown = True
|
|
|
|
|
|
def _validate_and_canonicalize(policy_graph, env):
|
|
if isinstance(policy_graph, dict):
|
|
for k, v in policy_graph.items():
|
|
if not isinstance(k, str):
|
|
raise ValueError(
|
|
"policy_graph keys must be strs, got {}".format(type(k)))
|
|
if not isinstance(v, tuple) or len(v) != 4:
|
|
raise ValueError(
|
|
"policy_graph values must be tuples of "
|
|
"(cls, obs_space, action_space, config), got {}".format(v))
|
|
if not issubclass(v[0], PolicyGraph):
|
|
raise ValueError(
|
|
"policy_graph tuple value 0 must be a rllib.PolicyGraph "
|
|
"class, got {}".format(v[0]))
|
|
if not isinstance(v[1], gym.Space):
|
|
raise ValueError(
|
|
"policy_graph tuple value 1 (observation_space) must be a "
|
|
"gym.Space, got {}".format(type(v[1])))
|
|
if not isinstance(v[2], gym.Space):
|
|
raise ValueError(
|
|
"policy_graph tuple value 2 (action_space) must be a "
|
|
"gym.Space, got {}".format(type(v[2])))
|
|
if not isinstance(v[3], dict):
|
|
raise ValueError(
|
|
"policy_graph tuple value 3 (config) must be a dict, "
|
|
"got {}".format(type(v[3])))
|
|
return policy_graph
|
|
elif not issubclass(policy_graph, PolicyGraph):
|
|
raise ValueError("policy_graph must be a rllib.PolicyGraph class")
|
|
else:
|
|
if (isinstance(env, MultiAgentEnv)
|
|
and not hasattr(env, "observation_space")):
|
|
raise ValueError(
|
|
"MultiAgentEnv must have observation_space defined if run "
|
|
"in a single-agent configuration.")
|
|
return {
|
|
DEFAULT_POLICY_ID: (policy_graph, env.observation_space,
|
|
env.action_space, {})
|
|
}
|
|
|
|
|
|
def _monitor(env, path):
|
|
return gym.wrappers.Monitor(env, path, resume=True)
|
|
|
|
|
|
def _has_tensorflow_graph(policy_dict):
|
|
for policy, _, _, _ in policy_dict.values():
|
|
if issubclass(policy, TFPolicyGraph):
|
|
return True
|
|
return False
|