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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from ray.rllib.env.external_env import ExternalEnv
from ray.rllib.env.vector_env import VectorEnv
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.utils.annotations import override
class AsyncVectorEnv(object):
"""The lowest-level env interface used by RLlib for sampling.
AsyncVectorEnv models multiple agents executing asynchronously in multiple
environments. A call to poll() returns observations from ready agents
keyed by their environment and agent ids, and actions for those agents
can be sent back via send_actions().
All other env types can be adapted to AsyncVectorEnv. RLlib handles these
conversions internally in PolicyEvaluator, for example:
gym.Env => rllib.VectorEnv => rllib.AsyncVectorEnv
rllib.MultiAgentEnv => rllib.AsyncVectorEnv
rllib.ExternalEnv => rllib.AsyncVectorEnv
action_space (gym.Space): Action space. This must be defined for
single-agent envs. Multi-agent envs can set this to None.
observation_space (gym.Space): Observation space. This must be defined
for single-agent envs. Multi-agent envs can set this to None.
>>> env = MyAsyncVectorEnv()
>>> obs, rewards, dones, infos, off_policy_actions = env.poll()
>>> print(obs)
"env_0": {
"car_0": [2.4, 1.6],
"car_1": [3.4, -3.2],
>>> env.send_actions(
"env_0": {
"car_0": 0,
"car_1": 1,
>>> obs, rewards, dones, infos, off_policy_actions = env.poll()
>>> print(obs)
"env_0": {
"car_0": [4.1, 1.7],
"car_1": [3.2, -4.2],
>>> print(dones)
"env_0": {
"__all__": False,
"car_0": False,
"car_1": True,
def wrap_async(env, make_env=None, num_envs=1):
"""Wraps any env type as needed to expose the async interface."""
if not isinstance(env, AsyncVectorEnv):
if isinstance(env, MultiAgentEnv):
env = _MultiAgentEnvToAsync(
make_env=make_env, existing_envs=[env], num_envs=num_envs)
elif isinstance(env, ExternalEnv):
if num_envs != 1:
raise ValueError(
"ExternalEnv does not currently support num_envs > 1.")
env = _ExternalEnvToAsync(env)
elif isinstance(env, VectorEnv):
env = _VectorEnvToAsync(env)
env = VectorEnv.wrap(
make_env=make_env, existing_envs=[env], num_envs=num_envs)
env = _VectorEnvToAsync(env)
assert isinstance(env, AsyncVectorEnv)
return env
def poll(self):
"""Returns observations from ready agents.
The returns are two-level dicts mapping from env_id to a dict of
agent_id to values. The number of agents and envs can vary over time.
obs (dict): New observations for each ready agent.
rewards (dict): Reward values for each ready agent. If the
episode is just started, the value will be None.
dones (dict): Done values for each ready agent. The special key
"__all__" is used to indicate env termination.
infos (dict): Info values for each ready agent.
off_policy_actions (dict): Agents may take off-policy actions. When
that happens, there will be an entry in this dict that contains
the taken action. There is no need to send_actions() for agents
that have already chosen off-policy actions.
raise NotImplementedError
def send_actions(self, action_dict):
"""Called to send actions back to running agents in this env.
Actions should be sent for each ready agent that returned observations
in the previous poll() call.
action_dict (dict): Actions values keyed by env_id and agent_id.
raise NotImplementedError
def try_reset(self, env_id):
"""Attempt to reset the env with the given id.
If the environment does not support synchronous reset, None can be
returned here.
obs (dict|None): Resetted observation or None if not supported.
return None
def get_unwrapped(self):
"""Return a reference to the underlying gym envs, if any.
envs (list): Underlying gym envs or [].
return []
# Fixed agent identifier when there is only the single agent in the env
_DUMMY_AGENT_ID = "single_agent"
def _with_dummy_agent_id(env_id_to_values, dummy_id=_DUMMY_AGENT_ID):
return {k: {dummy_id: v} for (k, v) in env_id_to_values.items()}
class _ExternalEnvToAsync(AsyncVectorEnv):
"""Internal adapter of ExternalEnv to AsyncVectorEnv."""
def __init__(self, external_env, preprocessor=None):
self.external_env = external_env
self.prep = preprocessor
self.action_space = external_env.action_space
if preprocessor:
self.observation_space = preprocessor.observation_space
self.observation_space = external_env.observation_space
def poll(self):
with self.external_env._results_avail_condition:
results = self._poll()
while len(results[0]) == 0:
results = self._poll()
if not self.external_env.isAlive():
raise Exception("Serving thread has stopped.")
limit = self.external_env._max_concurrent_episodes
assert len(results[0]) < limit, \
("Too many concurrent episodes, were some leaked? This "
"ExternalEnv was created with max_concurrent={}".format(limit))
return results
def send_actions(self, action_dict):
for eid, action in action_dict.items():
def _poll(self):
all_obs, all_rewards, all_dones, all_infos = {}, {}, {}, {}
off_policy_actions = {}
for eid, episode in self.external_env._episodes.copy().items():
data = episode.get_data()
if episode.cur_done:
del self.external_env._episodes[eid]
if data:
if self.prep:
all_obs[eid] = self.prep.transform(data["obs"])
all_obs[eid] = data["obs"]
all_rewards[eid] = data["reward"]
all_dones[eid] = data["done"]
all_infos[eid] = data["info"]
if "off_policy_action" in data:
off_policy_actions[eid] = data["off_policy_action"]
return _with_dummy_agent_id(all_obs), \
_with_dummy_agent_id(all_rewards), \
_with_dummy_agent_id(all_dones, "__all__"), \
_with_dummy_agent_id(all_infos), \
class _VectorEnvToAsync(AsyncVectorEnv):
"""Internal adapter of VectorEnv to AsyncVectorEnv.
We assume the caller will always send the full vector of actions in each
call to send_actions(), and that they call reset_at() on all completed
environments before calling send_actions().
def __init__(self, vector_env):
self.vector_env = vector_env
self.action_space = vector_env.action_space
self.observation_space = vector_env.observation_space
self.num_envs = vector_env.num_envs
self.new_obs = None # lazily initialized
self.cur_rewards = [None for _ in range(self.num_envs)]
self.cur_dones = [False for _ in range(self.num_envs)]
self.cur_infos = [None for _ in range(self.num_envs)]
def poll(self):
if self.new_obs is None:
self.new_obs = self.vector_env.vector_reset()
new_obs = dict(enumerate(self.new_obs))
rewards = dict(enumerate(self.cur_rewards))
dones = dict(enumerate(self.cur_dones))
infos = dict(enumerate(self.cur_infos))
self.new_obs = []
self.cur_rewards = []
self.cur_dones = []
self.cur_infos = []
return _with_dummy_agent_id(new_obs), \
_with_dummy_agent_id(rewards), \
_with_dummy_agent_id(dones, "__all__"), \
_with_dummy_agent_id(infos), {}
def send_actions(self, action_dict):
action_vector = [None] * self.num_envs
for i in range(self.num_envs):
action_vector[i] = action_dict[i][_DUMMY_AGENT_ID]
self.new_obs, self.cur_rewards, self.cur_dones, self.cur_infos = \
def try_reset(self, env_id):
return {_DUMMY_AGENT_ID: self.vector_env.reset_at(env_id)}
def get_unwrapped(self):
return self.vector_env.get_unwrapped()
class _MultiAgentEnvToAsync(AsyncVectorEnv):
"""Internal adapter of MultiAgentEnv to AsyncVectorEnv.
This also supports vectorization if num_envs > 1.
def __init__(self, make_env, existing_envs, num_envs):
"""Wrap existing multi-agent envs.
make_env (func|None): Factory that produces a new multiagent env.
Must be defined if the number of existing envs is less than
existing_envs (list): List of existing multiagent envs.
num_envs (int): Desired num multiagent envs to keep total.
self.make_env = make_env
self.envs = existing_envs
self.num_envs = num_envs
self.dones = set()
while len(self.envs) < self.num_envs:
for env in self.envs:
assert isinstance(env, MultiAgentEnv)
self.env_states = [_MultiAgentEnvState(env) for env in self.envs]
def poll(self):
obs, rewards, dones, infos = {}, {}, {}, {}
for i, env_state in enumerate(self.env_states):
obs[i], rewards[i], dones[i], infos[i] = env_state.poll()
return obs, rewards, dones, infos, {}
def send_actions(self, action_dict):
for env_id, agent_dict in action_dict.items():
if env_id in self.dones:
raise ValueError("Env {} is already done".format(env_id))
env = self.envs[env_id]
obs, rewards, dones, infos = env.step(agent_dict)
assert isinstance(obs, dict), "Not a multi-agent obs"
assert isinstance(rewards, dict), "Not a multi-agent reward"
assert isinstance(dones, dict), "Not a multi-agent return"
assert isinstance(infos, dict), "Not a multi-agent info"
if set(obs.keys()) != set(rewards.keys()):
raise ValueError(
"Key set for obs and rewards must be the same: "
"{} vs {}".format(obs.keys(), rewards.keys()))
if set(obs.keys()) != set(infos.keys()):
raise ValueError("Key set for obs and infos must be the same: "
"{} vs {}".format(obs.keys(), infos.keys()))
if dones["__all__"]:
self.env_states[env_id].observe(obs, rewards, dones, infos)
def try_reset(self, env_id):
obs = self.env_states[env_id].reset()
assert isinstance(obs, dict), "Not a multi-agent obs"
if obs is not None and env_id in self.dones:
return obs
class _MultiAgentEnvState(object):
def __init__(self, env):
assert isinstance(env, MultiAgentEnv)
self.env = env
def poll(self):
obs, rew, dones, info = (self.last_obs, self.last_rewards,
self.last_dones, self.last_infos)
self.last_obs = {}
self.last_rewards = {}
self.last_dones = {"__all__": False}
self.last_infos = {}
return obs, rew, dones, info
def observe(self, obs, rewards, dones, infos):
self.last_obs = obs
self.last_rewards = rewards
self.last_dones = dones
self.last_infos = infos
def reset(self):
self.last_obs = self.env.reset()
self.last_rewards = {
agent_id: None
for agent_id in self.last_obs.keys()
self.last_dones = {
agent_id: False
for agent_id in self.last_obs.keys()
self.last_infos = {agent_id: {} for agent_id in self.last_obs.keys()}
self.last_dones["__all__"] = False
return self.last_obs