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.
124 lines
3.8 KiB
Python
124 lines
3.8 KiB
Python
import logging
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import pickle
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from ray.rllib.utils.annotations import PublicAPI
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logger = logging.getLogger(__name__)
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try:
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import requests # `requests` is not part of stdlib.
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except ImportError:
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requests = None
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logger.warning(
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"Couldn't import `requests` library. Be sure to install it on"
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" the client side.")
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@PublicAPI
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class PolicyClient:
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"""REST client to interact with a RLlib policy server."""
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START_EPISODE = "START_EPISODE"
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GET_ACTION = "GET_ACTION"
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LOG_ACTION = "LOG_ACTION"
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LOG_RETURNS = "LOG_RETURNS"
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END_EPISODE = "END_EPISODE"
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@PublicAPI
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def __init__(self, address):
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self._address = address
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@PublicAPI
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def start_episode(self, episode_id=None, training_enabled=True):
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"""Record the start of an episode.
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Arguments:
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episode_id (str): Unique string id for the episode or None for
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it to be auto-assigned.
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training_enabled (bool): Whether to use experiences for this
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episode to improve the policy.
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Returns:
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episode_id (str): Unique string id for the episode.
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"""
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return self._send({
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"episode_id": episode_id,
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"command": PolicyClient.START_EPISODE,
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"training_enabled": training_enabled,
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})["episode_id"]
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@PublicAPI
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def get_action(self, episode_id, observation):
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"""Record an observation and get the on-policy action.
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Arguments:
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episode_id (str): Episode id returned from start_episode().
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observation (obj): Current environment observation.
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Returns:
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action (obj): Action from the env action space.
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"""
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return self._send({
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"command": PolicyClient.GET_ACTION,
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"observation": observation,
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"episode_id": episode_id,
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})["action"]
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@PublicAPI
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def log_action(self, episode_id, observation, action):
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"""Record an observation and (off-policy) action taken.
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Arguments:
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episode_id (str): Episode id returned from start_episode().
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observation (obj): Current environment observation.
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action (obj): Action for the observation.
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"""
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self._send({
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"command": PolicyClient.LOG_ACTION,
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"observation": observation,
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"action": action,
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"episode_id": episode_id,
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})
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@PublicAPI
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def log_returns(self, episode_id, reward, info=None):
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"""Record returns from the environment.
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The reward will be attributed to the previous action taken by the
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episode. Rewards accumulate until the next action. If no reward is
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logged before the next action, a reward of 0.0 is assumed.
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Arguments:
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episode_id (str): Episode id returned from start_episode().
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reward (float): Reward from the environment.
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"""
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self._send({
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"command": PolicyClient.LOG_RETURNS,
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"reward": reward,
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"info": info,
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"episode_id": episode_id,
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})
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@PublicAPI
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def end_episode(self, episode_id, observation):
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"""Record the end of an episode.
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Arguments:
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episode_id (str): Episode id returned from start_episode().
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observation (obj): Current environment observation.
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"""
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self._send({
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"command": PolicyClient.END_EPISODE,
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"observation": observation,
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"episode_id": episode_id,
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})
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def _send(self, data):
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payload = pickle.dumps(data)
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response = requests.post(self._address, data=payload)
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if response.status_code != 200:
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logger.error("Request failed {}: {}".format(response.text, data))
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response.raise_for_status()
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parsed = pickle.loads(response.content)
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return parsed
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