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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import queue
import threading
import uuid
class ExternalEnv(threading.Thread):
"""An environment that interfaces with external agents.
Unlike simulator envs, control is inverted. The environment queries the
policy to obtain actions and logs observations and rewards for training.
This is in contrast to gym.Env, where the algorithm drives the simulation
through env.step() calls.
You can use ExternalEnv as the backend for policy serving (by serving HTTP
requests in the run loop), for ingesting offline logs data (by reading
offline transitions in the run loop), or other custom use cases not easily
expressed through gym.Env.
ExternalEnv supports both on-policy actions (through self.get_action()),
and off-policy actions (through self.log_action()).
This env is thread-safe, but individual episodes must be executed serially.
Attributes:
action_space (gym.Space): Action space.
observation_space (gym.Space): Observation space.
Examples:
>>> register_env("my_env", lambda config: YourExternalEnv(config))
>>> agent = DQNAgent(env="my_env")
>>> while True:
print(agent.train())
"""
def __init__(self, action_space, observation_space, max_concurrent=100):
"""Initialize an external env.
ExternalEnv subclasses must call this during their __init__.
Arguments:
action_space (gym.Space): Action space of the env.
observation_space (gym.Space): Observation space of the env.
max_concurrent (int): Max number of active episodes to allow at
once. Exceeding this limit raises an error.
"""
threading.Thread.__init__(self)
self.daemon = True
self.action_space = action_space
self.observation_space = observation_space
self._episodes = {}
self._finished = set()
self._results_avail_condition = threading.Condition()
self._max_concurrent_episodes = max_concurrent
def run(self):
"""Override this to implement the run loop.
Your loop should continuously:
1. Call self.start_episode(episode_id)
2. Call self.get_action(episode_id, obs)
-or-
self.log_action(episode_id, obs, action)
3. Call self.log_returns(episode_id, reward)
4. Call self.end_episode(episode_id, obs)
5. Wait if nothing to do.
Multiple episodes may be started at the same time.
"""
raise NotImplementedError
def start_episode(self, episode_id=None, training_enabled=True):
"""Record the start of an episode.
Arguments:
episode_id (str): Unique string id for the episode or None for
it to be auto-assigned.
training_enabled (bool): Whether to use experiences for this
episode to improve the policy.
Returns:
episode_id (str): Unique string id for the episode.
"""
if episode_id is None:
episode_id = uuid.uuid4().hex
if episode_id in self._finished:
raise ValueError(
"Episode {} has already completed.".format(episode_id))
if episode_id in self._episodes:
raise ValueError(
"Episode {} is already started".format(episode_id))
self._episodes[episode_id] = _ExternalEnvEpisode(
episode_id, self._results_avail_condition, training_enabled)
return episode_id
def get_action(self, episode_id, observation):
"""Record an observation and get the on-policy action.
Arguments:
episode_id (str): Episode id returned from start_episode().
observation (obj): Current environment observation.
Returns:
action (obj): Action from the env action space.
"""
episode = self._get(episode_id)
return episode.wait_for_action(observation)
def log_action(self, episode_id, observation, action):
"""Record an observation and (off-policy) action taken.
Arguments:
episode_id (str): Episode id returned from start_episode().
observation (obj): Current environment observation.
action (obj): Action for the observation.
"""
episode = self._get(episode_id)
episode.log_action(observation, action)
def log_returns(self, episode_id, reward, info=None):
"""Record returns from the environment.
The reward will be attributed to the previous action taken by the
episode. Rewards accumulate until the next action. If no reward is
logged before the next action, a reward of 0.0 is assumed.
Arguments:
episode_id (str): Episode id returned from start_episode().
reward (float): Reward from the environment.
info (dict): Optional info dict.
"""
episode = self._get(episode_id)
episode.cur_reward += reward
if info:
episode.cur_info = info or {}
def end_episode(self, episode_id, observation):
"""Record the end of an episode.
Arguments:
episode_id (str): Episode id returned from start_episode().
observation (obj): Current environment observation.
"""
episode = self._get(episode_id)
self._finished.add(episode.episode_id)
episode.done(observation)
def _get(self, episode_id):
"""Get a started episode or raise an error."""
if episode_id in self._finished:
raise ValueError(
"Episode {} has already completed.".format(episode_id))
if episode_id not in self._episodes:
raise ValueError("Episode {} not found.".format(episode_id))
return self._episodes[episode_id]
class _ExternalEnvEpisode(object):
"""Tracked state for each active episode."""
def __init__(self, episode_id, results_avail_condition, training_enabled):
self.episode_id = episode_id
self.results_avail_condition = results_avail_condition
self.training_enabled = training_enabled
self.data_queue = queue.Queue()
self.action_queue = queue.Queue()
self.new_observation = None
self.new_action = None
self.cur_reward = 0.0
self.cur_done = False
self.cur_info = {}
def get_data(self):
if self.data_queue.empty():
return None
return self.data_queue.get_nowait()
def log_action(self, observation, action):
self.new_observation = observation
self.new_action = action
self._send()
self.action_queue.get(True, timeout=60.0)
def wait_for_action(self, observation):
self.new_observation = observation
self._send()
return self.action_queue.get(True, timeout=60.0)
def done(self, observation):
self.new_observation = observation
self.cur_done = True
self._send()
def _send(self):
item = {
"obs": self.new_observation,
"reward": self.cur_reward,
"done": self.cur_done,
"info": self.cur_info,
}
if self.new_action is not None:
item["off_policy_action"] = self.new_action
if not self.training_enabled:
item["info"]["training_enabled"] = False
self.new_observation = None
self.new_action = None
self.cur_reward = 0.0
with self.results_avail_condition:
self.data_queue.put_nowait(item)
self.results_avail_condition.notify()