mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
854 lines
35 KiB
Python
854 lines
35 KiB
Python
import random
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import numpy as np
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import gym
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import logging
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import pickle
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import ray
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from ray.rllib.env.atari_wrappers import wrap_deepmind, is_atari
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from ray.rllib.env.base_env import BaseEnv
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from ray.rllib.env.env_context import EnvContext
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from ray.rllib.env.external_env import ExternalEnv
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from ray.rllib.env.multi_agent_env import MultiAgentEnv
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from ray.rllib.env.external_multi_agent_env import ExternalMultiAgentEnv
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from ray.rllib.env.vector_env import VectorEnv
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from ray.rllib.evaluation.interface import EvaluatorInterface
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from ray.rllib.evaluation.sampler import AsyncSampler, SyncSampler
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from ray.rllib.policy.sample_batch import MultiAgentBatch, DEFAULT_POLICY_ID
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from ray.rllib.policy.policy import Policy
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from ray.rllib.policy.tf_policy import TFPolicy
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from ray.rllib.offline import NoopOutput, IOContext, OutputWriter, InputReader
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from ray.rllib.offline.is_estimator import ImportanceSamplingEstimator
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from ray.rllib.offline.wis_estimator import WeightedImportanceSamplingEstimator
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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, DeveloperAPI
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from ray.rllib.utils.debug import disable_log_once_globally, log_once, \
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summarize, enable_periodic_logging
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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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from ray.rllib.utils import try_import_tf, try_import_torch
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tf = try_import_tf()
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torch, _ = try_import_torch()
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logger = logging.getLogger(__name__)
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# Handle to the current rollout worker, which will be set to the most recently
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# created RolloutWorker in this process. This can be helpful to access in
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# custom env or policy classes for debugging or advanced use cases.
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_global_worker = None
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@DeveloperAPI
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def get_global_worker():
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"""Returns a handle to the active rollout worker in this process."""
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global _global_worker
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return _global_worker
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@DeveloperAPI
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class RolloutWorker(EvaluatorInterface):
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"""Common experience collection class.
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This class wraps a policy 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 rollout worker and using it to collect experiences.
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>>> worker = RolloutWorker(
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... env_creator=lambda _: gym.make("CartPole-v0"),
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... policy=PGTFPolicy)
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>>> print(worker.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 a multi-agent rollout worker
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>>> worker = RolloutWorker(
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... env_creator=lambda _: MultiAgentTrafficGrid(num_cars=25),
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... policies={
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... # Use an ensemble of two policies for car agents
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... "car_policy1":
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... (PGTFPolicy, Box(...), Discrete(...), {"gamma": 0.99}),
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... "car_policy2":
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... (PGTFPolicy, 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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... (PGTFPolicy, 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(worker.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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@DeveloperAPI
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@classmethod
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def as_remote(cls,
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num_cpus=None,
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num_gpus=None,
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memory=None,
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object_store_memory=None,
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resources=None):
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return ray.remote(
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num_cpus=num_cpus,
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num_gpus=num_gpus,
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memory=memory,
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object_store_memory=object_store_memory,
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resources=resources)(cls)
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@DeveloperAPI
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def __init__(self,
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env_creator,
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policy,
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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=frozenset([]),
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output_creator=lambda ioctx: NoopOutput(),
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remote_worker_envs=False,
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remote_env_batch_wait_ms=0,
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soft_horizon=False,
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no_done_at_end=False,
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seed=None,
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_fake_sampler=False):
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"""Initialize a rollout worker.
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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 (class|dict): Either a class implementing
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Policy, or a dictionary of policy id strings to
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(Policy, 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 TFPolicy.
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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 worker.
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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`.
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worker_index (int): For remote workers, 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 (list): How to evaluate the policy performance.
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This only makes sense to set when the input is reading offline
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data. The possible values include:
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- "is": the step-wise importance sampling estimator.
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- "wis": the weighted step-wise is estimator.
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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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remote_worker_envs (bool): If using num_envs > 1, whether to create
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those new envs in remote processes instead of in the current
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process. This adds overheads, but can make sense if your envs
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remote_env_batch_wait_ms (float): Timeout that remote workers
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are waiting when polling environments. 0 (continue when at
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least one env is ready) is a reasonable default, but optimal
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value could be obtained by measuring your environment
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step / reset and model inference perf.
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soft_horizon (bool): Calculate rewards but don't reset the
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environment when the horizon is hit.
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no_done_at_end (bool): Ignore the done=True at the end of the
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episode and instead record done=False.
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seed (int): Set the seed of both np and tf to this value to
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to ensure each remote worker has unique exploration behavior.
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_fake_sampler (bool): Use a fake (inf speed) sampler for testing.
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"""
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global _global_worker
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_global_worker = self
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policy_config = policy_config or {}
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if (tf and policy_config.get("eager")
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and not policy_config.get("no_eager_on_workers")):
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tf.enable_eager_execution()
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if log_level:
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logging.getLogger("ray.rllib").setLevel(log_level)
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if worker_index > 1:
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disable_log_once_globally() # only need 1 worker to log
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elif log_level == "DEBUG":
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enable_periodic_logging()
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env_context = EnvContext(env_config or {}, worker_index)
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self.policy_config = policy_config
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self.callbacks = callbacks or {}
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self.worker_index = worker_index
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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("Policy mapping function not callable?")
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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.last_batch = None
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self._fake_sampler = _fake_sampler
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self.env = _validate_env(env_creator(env_context))
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if isinstance(self.env, MultiAgentEnv) or \
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isinstance(self.env, BaseEnv):
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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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from gym import wrappers
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env = wrappers.Monitor(env, monitor_path, resume=True)
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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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from gym import wrappers
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env = wrappers.Monitor(env, monitor_path, resume=True)
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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(
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env_context.copy_with_overrides(
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vector_index=vector_index, remote=remote_worker_envs)))
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self.tf_sess = None
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policy_dict = _validate_and_canonicalize(policy, self.env)
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self.policies_to_train = policies_to_train or list(policy_dict.keys())
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# set numpy and python seed
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if seed is not None:
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np.random.seed(seed)
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random.seed(seed)
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if not hasattr(self.env, "seed"):
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raise ValueError("Env doesn't support env.seed(): {}".format(
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self.env))
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self.env.seed(seed)
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try:
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assert torch is not None
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torch.manual_seed(seed)
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except AssertionError:
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logger.info("Could not seed torch")
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if _has_tensorflow_graph(policy_dict) and not (tf and
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tf.executing_eagerly()):
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if not tf:
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raise ImportError("Could not import tensorflow")
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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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# set graph-level seed
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if seed is not None:
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tf.set_random_seed(seed)
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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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if (ray.is_initialized()
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and ray.worker._mode() != ray.worker.LOCAL_MODE):
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if not ray.get_gpu_ids():
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logger.debug(
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"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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elif not tf.test.is_gpu_available():
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raise RuntimeError(
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"GPUs were assigned to this worker by Ray, but "
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"TensorFlow reports GPU acceleration is disabled. "
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"This could be due to a bad CUDA or TF installation.")
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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, ExternalMultiAgentEnv))
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or isinstance(self.env, BaseEnv)):
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raise ValueError(
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"Have multiple policies {}, but the env ".format(
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self.policy_map) +
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"{} is not a subclass of BaseEnv, MultiAgentEnv or "
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"ExternalMultiAgentEnv?".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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if self.worker_index == 0:
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logger.info("Built filter map: {}".format(self.filters))
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# Always use vector env for consistency even if num_envs = 1
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self.async_env = BaseEnv.to_base_env(
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self.env,
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make_env=make_env,
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num_envs=num_envs,
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remote_envs=remote_worker_envs,
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remote_env_batch_wait_ms=remote_env_batch_wait_ms)
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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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self.io_context = IOContext(log_dir, policy_config, worker_index, self)
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self.reward_estimators = []
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for method in input_evaluation:
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if 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 method == "is":
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ise = ImportanceSamplingEstimator.create(self.io_context)
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self.reward_estimators.append(ise)
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elif method == "wis":
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wise = WeightedImportanceSamplingEstimator.create(
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self.io_context)
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self.reward_estimators.append(wise)
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else:
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raise ValueError(
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"Unknown evaluation method: {}".format(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="simulation" in input_evaluation,
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soft_horizon=soft_horizon,
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no_done_at_end=no_done_at_end)
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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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soft_horizon=soft_horizon,
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no_done_at_end=no_done_at_end)
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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(
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|
"Created rollout worker with env {} ({}), policies {}".format(
|
|
self.async_env, self.env, self.policy_map))
|
|
|
|
@override(EvaluatorInterface)
|
|
def sample(self):
|
|
"""Evaluate the current policies and return a batch of experiences.
|
|
|
|
Return:
|
|
SampleBatch|MultiAgentBatch from evaluating the current policies.
|
|
"""
|
|
|
|
if self._fake_sampler and self.last_batch is not None:
|
|
return self.last_batch
|
|
|
|
if log_once("sample_start"):
|
|
logger.info("Generating sample batch of size {}".format(
|
|
self.sample_batch_size))
|
|
|
|
batches = [self.input_reader.next()]
|
|
steps_so_far = batches[0].count
|
|
|
|
# In truncate_episodes mode, never pull more than 1 batch per env.
|
|
# This avoids over-running the target batch size.
|
|
if self.batch_mode == "truncate_episodes":
|
|
max_batches = self.num_envs
|
|
else:
|
|
max_batches = float("inf")
|
|
|
|
while steps_so_far < self.sample_batch_size and len(
|
|
batches) < max_batches:
|
|
batch = self.input_reader.next()
|
|
steps_so_far += batch.count
|
|
batches.append(batch)
|
|
batch = batches[0].concat_samples(batches)
|
|
|
|
if self.callbacks.get("on_sample_end"):
|
|
self.callbacks["on_sample_end"]({"worker": self, "samples": batch})
|
|
|
|
# Always do writes prior to compression for consistency and to allow
|
|
# for better compression inside the writer.
|
|
self.output_writer.write(batch)
|
|
|
|
# Do off-policy estimation if needed
|
|
if self.reward_estimators:
|
|
for sub_batch in batch.split_by_episode():
|
|
for estimator in self.reward_estimators:
|
|
estimator.process(sub_batch)
|
|
|
|
if log_once("sample_end"):
|
|
logger.info("Completed sample batch:\n\n{}\n".format(
|
|
summarize(batch)))
|
|
|
|
if self.compress_observations == "bulk":
|
|
batch.compress(bulk=True)
|
|
elif self.compress_observations:
|
|
batch.compress()
|
|
|
|
if self._fake_sampler:
|
|
self.last_batch = batch
|
|
return batch
|
|
|
|
@DeveloperAPI
|
|
@ray.method(num_return_vals=2)
|
|
def sample_with_count(self):
|
|
"""Same as sample() but returns the count as a separate future."""
|
|
batch = self.sample()
|
|
return batch, batch.count
|
|
|
|
@override(EvaluatorInterface)
|
|
def get_weights(self, policies=None):
|
|
if policies is None:
|
|
policies = self.policy_map.keys()
|
|
return {
|
|
pid: policy.get_weights()
|
|
for pid, policy in self.policy_map.items() if pid in policies
|
|
}
|
|
|
|
@override(EvaluatorInterface)
|
|
def set_weights(self, weights):
|
|
for pid, w in weights.items():
|
|
self.policy_map[pid].set_weights(w)
|
|
|
|
@override(EvaluatorInterface)
|
|
def compute_gradients(self, samples):
|
|
if log_once("compute_gradients"):
|
|
logger.info("Compute gradients on:\n\n{}\n".format(
|
|
summarize(samples)))
|
|
if isinstance(samples, MultiAgentBatch):
|
|
grad_out, info_out = {}, {}
|
|
if self.tf_sess is not None:
|
|
builder = TFRunBuilder(self.tf_sess, "compute_gradients")
|
|
for pid, batch in samples.policy_batches.items():
|
|
if pid not in self.policies_to_train:
|
|
continue
|
|
grad_out[pid], info_out[pid] = (
|
|
self.policy_map[pid]._build_compute_gradients(
|
|
builder, batch))
|
|
grad_out = {k: builder.get(v) for k, v in grad_out.items()}
|
|
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
|
|
grad_out[pid], info_out[pid] = (
|
|
self.policy_map[pid].compute_gradients(batch))
|
|
else:
|
|
grad_out, info_out = (
|
|
self.policy_map[DEFAULT_POLICY_ID].compute_gradients(samples))
|
|
info_out["batch_count"] = samples.count
|
|
if log_once("grad_out"):
|
|
logger.info("Compute grad info:\n\n{}\n".format(
|
|
summarize(info_out)))
|
|
return grad_out, info_out
|
|
|
|
@override(EvaluatorInterface)
|
|
def apply_gradients(self, grads):
|
|
if log_once("apply_gradients"):
|
|
logger.info("Apply gradients:\n\n{}\n".format(summarize(grads)))
|
|
if isinstance(grads, dict):
|
|
if self.tf_sess is not None:
|
|
builder = TFRunBuilder(self.tf_sess, "apply_gradients")
|
|
outputs = {
|
|
pid: self.policy_map[pid]._build_apply_gradients(
|
|
builder, grad)
|
|
for pid, grad in grads.items()
|
|
}
|
|
return {k: builder.get(v) for k, v in outputs.items()}
|
|
else:
|
|
return {
|
|
pid: self.policy_map[pid].apply_gradients(g)
|
|
for pid, g in grads.items()
|
|
}
|
|
else:
|
|
return self.policy_map[DEFAULT_POLICY_ID].apply_gradients(grads)
|
|
|
|
@override(EvaluatorInterface)
|
|
def learn_on_batch(self, samples):
|
|
if log_once("learn_on_batch"):
|
|
logger.info(
|
|
"Training on concatenated sample batches:\n\n{}\n".format(
|
|
summarize(samples)))
|
|
if isinstance(samples, MultiAgentBatch):
|
|
info_out = {}
|
|
to_fetch = {}
|
|
if self.tf_sess is not None:
|
|
builder = TFRunBuilder(self.tf_sess, "learn_on_batch")
|
|
else:
|
|
builder = None
|
|
for pid, batch in samples.policy_batches.items():
|
|
if pid not in self.policies_to_train:
|
|
continue
|
|
policy = self.policy_map[pid]
|
|
if builder and hasattr(policy, "_build_learn_on_batch"):
|
|
to_fetch[pid] = policy._build_learn_on_batch(
|
|
builder, batch)
|
|
else:
|
|
info_out[pid] = policy.learn_on_batch(batch)
|
|
info_out.update({k: builder.get(v) for k, v in to_fetch.items()})
|
|
else:
|
|
info_out = self.policy_map[DEFAULT_POLICY_ID].learn_on_batch(
|
|
samples)
|
|
if log_once("learn_out"):
|
|
logger.debug("Training out:\n\n{}\n".format(summarize(info_out)))
|
|
return info_out
|
|
|
|
@DeveloperAPI
|
|
def get_metrics(self):
|
|
"""Returns a list of new RolloutMetric objects from evaluation."""
|
|
|
|
out = self.sampler.get_metrics()
|
|
for m in self.reward_estimators:
|
|
out.extend(m.get_metrics())
|
|
return out
|
|
|
|
@DeveloperAPI
|
|
def foreach_env(self, func):
|
|
"""Apply the given function to each underlying env instance."""
|
|
|
|
envs = self.async_env.get_unwrapped()
|
|
if not envs:
|
|
return [func(self.async_env)]
|
|
else:
|
|
return [func(e) for e in envs]
|
|
|
|
@DeveloperAPI
|
|
def get_policy(self, policy_id=DEFAULT_POLICY_ID):
|
|
"""Return policy for the specified id, or None.
|
|
|
|
Arguments:
|
|
policy_id (str): id of policy to return.
|
|
"""
|
|
|
|
return self.policy_map.get(policy_id)
|
|
|
|
@DeveloperAPI
|
|
def for_policy(self, func, policy_id=DEFAULT_POLICY_ID):
|
|
"""Apply the given function to the specified policy."""
|
|
|
|
return func(self.policy_map[policy_id])
|
|
|
|
@DeveloperAPI
|
|
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()]
|
|
|
|
@DeveloperAPI
|
|
def foreach_trainable_policy(self, func):
|
|
"""
|
|
Applies the given function to each (policy, policy_id) tuple, which
|
|
can be found in `self.policies_to_train`.
|
|
|
|
Args:
|
|
func (callable): A function - taking a Policy and its ID - that is
|
|
called on all Policies within `self.policies_to_train`.
|
|
|
|
Returns:
|
|
List[any]: The list of n return values of all
|
|
`func([policy], [ID])`-calls.
|
|
"""
|
|
return [
|
|
func(policy, pid) for pid, policy in self.policy_map.items()
|
|
if pid in self.policies_to_train
|
|
]
|
|
|
|
@DeveloperAPI
|
|
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])
|
|
|
|
@DeveloperAPI
|
|
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
|
|
|
|
@DeveloperAPI
|
|
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})
|
|
|
|
@DeveloperAPI
|
|
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)
|
|
|
|
@DeveloperAPI
|
|
def set_global_vars(self, global_vars):
|
|
self.foreach_policy(lambda p, _: p.on_global_var_update(global_vars))
|
|
|
|
@DeveloperAPI
|
|
def export_policy_model(self, export_dir, policy_id=DEFAULT_POLICY_ID):
|
|
self.policy_map[policy_id].export_model(export_dir)
|
|
|
|
@DeveloperAPI
|
|
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)
|
|
|
|
@DeveloperAPI
|
|
def stop(self):
|
|
self.async_env.stop()
|
|
|
|
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()):
|
|
logger.debug("Creating policy for {}".format(name))
|
|
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. "
|
|
"Please preprocess these observations with a "
|
|
"Tuple|DictFlatteningPreprocessor.")
|
|
if tf and tf.executing_eagerly():
|
|
if hasattr(cls, "as_eager"):
|
|
cls = cls.as_eager()
|
|
if policy_config["eager_tracing"]:
|
|
cls = cls.with_tracing()
|
|
elif not issubclass(cls, TFPolicy):
|
|
pass # could be some other type of policy
|
|
else:
|
|
raise ValueError("This policy does not support eager "
|
|
"execution: {}".format(cls))
|
|
if tf:
|
|
with tf.variable_scope(name):
|
|
policy_map[name] = cls(obs_space, act_space, merged_conf)
|
|
else:
|
|
policy_map[name] = cls(obs_space, act_space, merged_conf)
|
|
if self.worker_index == 0:
|
|
logger.info("Built policy map: {}".format(policy_map))
|
|
logger.info("Built preprocessor map: {}".format(preprocessors))
|
|
return policy_map, preprocessors
|
|
|
|
def __del__(self):
|
|
if hasattr(self, "sampler") and isinstance(self.sampler, AsyncSampler):
|
|
self.sampler.shutdown = True
|
|
|
|
|
|
def _validate_and_canonicalize(policy, env):
|
|
if isinstance(policy, dict):
|
|
_validate_multiagent_config(policy)
|
|
return policy
|
|
elif not issubclass(policy, Policy):
|
|
raise ValueError("policy must be a rllib.Policy 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, env.observation_space,
|
|
env.action_space, {})
|
|
}
|
|
|
|
|
|
def _validate_multiagent_config(policy, allow_none_graph=False):
|
|
for k, v in policy.items():
|
|
if not isinstance(k, str):
|
|
raise ValueError("policy keys must be strs, got {}".format(
|
|
type(k)))
|
|
if not isinstance(v, (tuple, list)) or len(v) != 4:
|
|
raise ValueError(
|
|
"policy values must be tuples/lists of "
|
|
"(cls or None, obs_space, action_space, config), got {}".
|
|
format(v))
|
|
if allow_none_graph and v[0] is None:
|
|
pass
|
|
elif not issubclass(v[0], Policy):
|
|
raise ValueError("policy tuple value 0 must be a rllib.Policy "
|
|
"class or None, got {}".format(v[0]))
|
|
if not isinstance(v[1], gym.Space):
|
|
raise ValueError(
|
|
"policy 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 tuple value 2 (action_space) must be a "
|
|
"gym.Space, got {}".format(type(v[2])))
|
|
if not isinstance(v[3], dict):
|
|
raise ValueError("policy tuple value 3 (config) must be a dict, "
|
|
"got {}".format(type(v[3])))
|
|
|
|
|
|
def _validate_env(env):
|
|
# allow this as a special case (assumed gym.Env)
|
|
if hasattr(env, "observation_space") and hasattr(env, "action_space"):
|
|
return env
|
|
|
|
allowed_types = [gym.Env, MultiAgentEnv, ExternalEnv, VectorEnv, BaseEnv]
|
|
if not any(isinstance(env, tpe) for tpe in allowed_types):
|
|
raise ValueError(
|
|
"Returned env should be an instance of gym.Env, MultiAgentEnv, "
|
|
"ExternalEnv, VectorEnv, or BaseEnv. The provided env creator "
|
|
"function returned {} ({}).".format(env, type(env)))
|
|
return env
|
|
|
|
|
|
def _has_tensorflow_graph(policy_dict):
|
|
for policy, _, _, _ in policy_dict.values():
|
|
if issubclass(policy, TFPolicy):
|
|
return True
|
|
return False
|