Source code for ray.rllib.env.base_env

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.external_multi_agent_env import ExternalMultiAgentEnv
from ray.rllib.env.vector_env import VectorEnv
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.utils.annotations import override, PublicAPI

ASYNC_RESET_RETURN = "async_reset_return"


[docs]@PublicAPI class BaseEnv(object): """The lowest-level env interface used by RLlib for sampling. BaseEnv 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 BaseEnv. RLlib handles these conversions internally in RolloutWorker, for example: gym.Env => rllib.VectorEnv => rllib.BaseEnv rllib.MultiAgentEnv => rllib.BaseEnv rllib.ExternalEnv => rllib.BaseEnv Attributes: 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. Examples: >>> env = MyBaseEnv() >>> 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_1": { "car_0": [8.0, 4.1], }, "env_2": { "car_0": [2.3, 3.3], "car_1": [1.4, -0.2], "car_3": [1.2, 0.1], }, } >>> env.send_actions( 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, }, ... } """
[docs] @staticmethod def to_base_env(env, make_env=None, num_envs=1, remote_envs=False, remote_env_batch_wait_ms=0): """Wraps any env type as needed to expose the async interface.""" from ray.rllib.env.remote_vector_env import RemoteVectorEnv if remote_envs and num_envs == 1: raise ValueError( "Remote envs only make sense to use if num_envs > 1 " "(i.e. vectorization is enabled).") if not isinstance(env, BaseEnv): if isinstance(env, MultiAgentEnv): if remote_envs: env = RemoteVectorEnv( make_env, num_envs, multiagent=True, remote_env_batch_wait_ms=remote_env_batch_wait_ms) else: env = _MultiAgentEnvToBaseEnv( make_env=make_env, existing_envs=[env], num_envs=num_envs) elif isinstance(env, ExternalMultiAgentEnv): if num_envs != 1: raise ValueError( "ExternalMultiAgentEnv does not currently support " "num_envs > 1.") env = _ExternalEnvToBaseEnv(env, multiagent=True) elif isinstance(env, ExternalEnv): if num_envs != 1: raise ValueError( "ExternalEnv does not currently support num_envs > 1.") env = _ExternalEnvToBaseEnv(env) elif isinstance(env, VectorEnv): env = _VectorEnvToBaseEnv(env) else: if remote_envs: env = RemoteVectorEnv( make_env, num_envs, multiagent=False, remote_env_batch_wait_ms=remote_env_batch_wait_ms) else: env = VectorEnv.wrap( make_env=make_env, existing_envs=[env], num_envs=num_envs, action_space=env.action_space, observation_space=env.observation_space) env = _VectorEnvToBaseEnv(env) assert isinstance(env, BaseEnv), env return env
[docs] @PublicAPI 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. Returns ------- 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
[docs] @PublicAPI 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. Arguments: action_dict (dict): Actions values keyed by env_id and agent_id. """ raise NotImplementedError
[docs] @PublicAPI 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. Returns: obs (dict|None): Resetted observation or None if not supported. """ return None
[docs] @PublicAPI def get_unwrapped(self): """Return a reference to the underlying gym envs, if any. Returns: envs (list): Underlying gym envs or []. """ return []
[docs] @PublicAPI def stop(self): """Releases all resources used.""" for env in self.get_unwrapped(): if hasattr(env, "close"): env.close()
# Fixed agent identifier when there is only the single agent in the env _DUMMY_AGENT_ID = "agent0" 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 _ExternalEnvToBaseEnv(BaseEnv): """Internal adapter of ExternalEnv to BaseEnv.""" def __init__(self, external_env, preprocessor=None, multiagent=False): self.external_env = external_env self.prep = preprocessor self.multiagent = multiagent self.action_space = external_env.action_space if preprocessor: self.observation_space = preprocessor.observation_space else: self.observation_space = external_env.observation_space external_env.start() @override(BaseEnv) def poll(self): with self.external_env._results_avail_condition: results = self._poll() while len(results[0]) == 0: self.external_env._results_avail_condition.wait() 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 @override(BaseEnv) def send_actions(self, action_dict): if self.multiagent: for env_id, actions in action_dict.items(): self.external_env._episodes[env_id].action_queue.put(actions) else: for env_id, action in action_dict.items(): self.external_env._episodes[env_id].action_queue.put( action[_DUMMY_AGENT_ID]) 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() cur_done = episode.cur_done_dict[ "__all__"] if self.multiagent else episode.cur_done if cur_done: del self.external_env._episodes[eid] if data: if self.prep: all_obs[eid] = self.prep.transform(data["obs"]) else: 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"] if self.multiagent: # ensure a consistent set of keys # rely on all_obs having all possible keys for now for eid, eid_dict in all_obs.items(): for agent_id in eid_dict.keys(): def fix(d, zero_val): if agent_id not in d[eid]: d[eid][agent_id] = zero_val fix(all_rewards, 0.0) fix(all_dones, False) fix(all_infos, {}) return (all_obs, all_rewards, all_dones, all_infos, off_policy_actions) else: 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), \ _with_dummy_agent_id(off_policy_actions) class _VectorEnvToBaseEnv(BaseEnv): """Internal adapter of VectorEnv to BaseEnv. 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)] @override(BaseEnv) 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), {} @override(BaseEnv) 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 = \ self.vector_env.vector_step(action_vector) @override(BaseEnv) def try_reset(self, env_id): return {_DUMMY_AGENT_ID: self.vector_env.reset_at(env_id)} @override(BaseEnv) def get_unwrapped(self): return self.vector_env.get_unwrapped() class _MultiAgentEnvToBaseEnv(BaseEnv): """Internal adapter of MultiAgentEnv to BaseEnv. This also supports vectorization if num_envs > 1. """ def __init__(self, make_env, existing_envs, num_envs): """Wrap existing multi-agent envs. Arguments: make_env (func|None): Factory that produces a new multiagent env. Must be defined if the number of existing envs is less than num_envs. 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: self.envs.append(self.make_env(len(self.envs))) for env in self.envs: assert isinstance(env, MultiAgentEnv) self.env_states = [_MultiAgentEnvState(env) for env in self.envs] @override(BaseEnv) 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, {} @override(BaseEnv) 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(infos).difference(set(obs)): raise ValueError("Key set for infos must be a subset of obs: " "{} vs {}".format(infos.keys(), obs.keys())) if "__all__" not in dones: raise ValueError( "In multi-agent environments, '__all__': True|False must " "be included in the 'done' dict: got {}.".format(dones)) if dones["__all__"]: self.dones.add(env_id) self.env_states[env_id].observe(obs, rewards, dones, infos) @override(BaseEnv) 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: self.dones.remove(env_id) return obs @override(BaseEnv) def get_unwrapped(self): return [state.env for state in self.env_states] class _MultiAgentEnvState(object): def __init__(self, env): assert isinstance(env, MultiAgentEnv) self.env = env self.initialized = False def poll(self): if not self.initialized: self.reset() self.initialized = True 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