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Source code for ding.envs.env_manager.subprocess_env_manager

from typing import Any, Union, List, Tuple, Dict, Callable, Optional
from multiprocessing import connection, get_context
from collections import namedtuple
from ditk import logging
import platform
import time
import copy
import gymnasium
import gym
import traceback
import torch
import pickle
import numpy as np
import treetensor.numpy as tnp
from easydict import EasyDict
from types import MethodType
from ding.data import ShmBufferContainer, ShmBuffer

from ding.envs.env import BaseEnvTimestep
from ding.utils import PropagatingThread, LockContextType, LockContext, ENV_MANAGER_REGISTRY, make_key_as_identifier, \
    remove_illegal_item, CloudPickleWrapper
from .base_env_manager import BaseEnvManager, EnvState, timeout_wrapper


def is_abnormal_timestep(timestep: namedtuple) -> bool:
    if isinstance(timestep.info, dict):
        return timestep.info.get('abnormal', False)
    elif isinstance(timestep.info, list) or isinstance(timestep.info, tuple):
        return timestep.info[0].get('abnormal', False) or timestep.info[1].get('abnormal', False)
    else:
        raise TypeError("invalid env timestep type: {}".format(type(timestep.info)))


[docs]@ENV_MANAGER_REGISTRY.register('async_subprocess') class AsyncSubprocessEnvManager(BaseEnvManager): """ Overview: Create an AsyncSubprocessEnvManager to manage multiple environments. Each Environment is run by a respective subprocess. Interfaces: seed, launch, ready_obs, step, reset, active_env """ config = dict( episode_num=float("inf"), max_retry=1, step_timeout=None, auto_reset=True, retry_type='reset', reset_timeout=None, retry_waiting_time=0.1, # subprocess specified args shared_memory=True, copy_on_get=True, context='spawn' if platform.system().lower() == 'windows' else 'fork', wait_num=2, step_wait_timeout=0.01, connect_timeout=60, reset_inplace=False, )
[docs] def __init__( self, env_fn: List[Callable], cfg: EasyDict = EasyDict({}), ) -> None: """ Overview: Initialize the AsyncSubprocessEnvManager. Arguments: - env_fn (:obj:`List[Callable]`): The function to create environment - cfg (:obj:`EasyDict`): Config .. note:: - wait_num: for each time the minimum number of env return to gather - step_wait_timeout: for each time the minimum number of env return to gather """ super().__init__(env_fn, cfg) self._shared_memory = self._cfg.shared_memory self._copy_on_get = self._cfg.copy_on_get self._context = self._cfg.context self._wait_num = self._cfg.wait_num self._step_wait_timeout = self._cfg.step_wait_timeout self._lock = LockContext(LockContextType.THREAD_LOCK) self._connect_timeout = self._cfg.connect_timeout self._async_args = { 'step': { 'wait_num': min(self._wait_num, self._env_num), 'timeout': self._step_wait_timeout } } self._reset_inplace = self._cfg.reset_inplace if not self._auto_reset: assert not self._reset_inplace, "reset_inplace is unavailable when auto_reset=False."
def _create_state(self) -> None: r""" Overview: Fork/spawn sub-processes(Call ``_create_env_subprocess``) and create pipes to transfer the data. """ self._env_episode_count = {env_id: 0 for env_id in range(self.env_num)} self._ready_obs = {env_id: None for env_id in range(self.env_num)} self._reset_param = {i: {} for i in range(self.env_num)} if self._shared_memory: obs_space = self._observation_space if isinstance(obs_space, (gym.spaces.Dict, gymnasium.spaces.Dict)): # For multi_agent case, such as multiagent_mujoco and petting_zoo mpe. # Now only for the case that each agent in the team have the same obs structure # and corresponding shape. shape = {k: v.shape for k, v in obs_space.spaces.items()} dtype = {k: v.dtype for k, v in obs_space.spaces.items()} else: shape = obs_space.shape dtype = obs_space.dtype self._obs_buffers = { env_id: ShmBufferContainer(dtype, shape, copy_on_get=self._copy_on_get) for env_id in range(self.env_num) } else: self._obs_buffers = {env_id: None for env_id in range(self.env_num)} self._pipe_parents, self._pipe_children = {}, {} self._subprocesses = {} for env_id in range(self.env_num): self._create_env_subprocess(env_id) self._waiting_env = {'step': set()} self._closed = False def _create_env_subprocess(self, env_id): # start a new one ctx = get_context(self._context) self._pipe_parents[env_id], self._pipe_children[env_id] = ctx.Pipe() self._subprocesses[env_id] = ctx.Process( # target=self.worker_fn, target=self.worker_fn_robust, args=( self._pipe_parents[env_id], self._pipe_children[env_id], CloudPickleWrapper(self._env_fn[env_id]), self._obs_buffers[env_id], self.method_name_list, self._reset_timeout, self._step_timeout, self._reset_inplace, ), daemon=True, name='subprocess_env_manager{}_{}'.format(env_id, time.time()) ) self._subprocesses[env_id].start() self._pipe_children[env_id].close() self._env_states[env_id] = EnvState.INIT if self._env_replay_path is not None: self._pipe_parents[env_id].send(['enable_save_replay', [self._env_replay_path[env_id]], {}]) self._pipe_parents[env_id].recv() @property def ready_env(self) -> List[int]: active_env = [i for i, s in self._env_states.items() if s == EnvState.RUN] return [i for i in active_env if i not in self._waiting_env['step']] @property def ready_obs(self) -> Dict[int, Any]: """ Overview: Get the next observations. Return: A dictionary with observations and their environment IDs. Note: The observations are returned in np.ndarray. Example: >>> obs_dict = env_manager.ready_obs >>> actions_dict = {env_id: model.forward(obs) for env_id, obs in obs_dict.items())} """ no_done_env_idx = [i for i, s in self._env_states.items() if s != EnvState.DONE] sleep_count = 0 while not any([self._env_states[i] == EnvState.RUN for i in no_done_env_idx]): if sleep_count != 0 and sleep_count % 10000 == 0: logging.warning( 'VEC_ENV_MANAGER: all the not done envs are resetting, sleep {} times'.format(sleep_count) ) time.sleep(0.001) sleep_count += 1 return {i: self._ready_obs[i] for i in self.ready_env} @property def ready_imgs(self, render_mode: Optional[str] = 'rgb_array') -> Dict[int, Any]: """ Overview: Get the next renderd frames. Return: A dictionary with rendered frames and their environment IDs. Note: The rendered frames are returned in np.ndarray. """ for i in self.ready_env: self._pipe_parents[i].send(['render', None, {'render_mode': render_mode}]) data = {i: self._pipe_parents[i].recv() for i in self.ready_env} self._check_data(data) return data
[docs] def launch(self, reset_param: Optional[Dict] = None) -> None: """ Overview: Set up the environments and their parameters. Arguments: - reset_param (:obj:`Optional[Dict]`): Dict of reset parameters for each environment, key is the env_id, \ value is the cooresponding reset parameters. """ assert self._closed, "please first close the env manager" if reset_param is not None: assert len(reset_param) == len(self._env_fn) self._create_state() self.reset(reset_param)
[docs] def reset(self, reset_param: Optional[Dict] = None) -> None: """ Overview: Reset the environments their parameters. Arguments: - reset_param (:obj:`List`): Dict of reset parameters for each environment, key is the env_id, \ value is the cooresponding reset parameters. """ self._check_closed() if reset_param is None: reset_env_list = [env_id for env_id in range(self._env_num)] else: reset_env_list = reset_param.keys() for env_id in reset_param: self._reset_param[env_id] = reset_param[env_id] # clear previous info for env_id in reset_env_list: if env_id in self._waiting_env['step']: self._pipe_parents[env_id].recv() self._waiting_env['step'].remove(env_id) sleep_count = 0 while any([self._env_states[i] == EnvState.RESET for i in reset_env_list]): if sleep_count != 0 and sleep_count % 10000 == 0: logging.warning( 'VEC_ENV_MANAGER: not all the envs finish resetting, sleep {} times'.format(sleep_count) ) time.sleep(0.001) sleep_count += 1 # reset env reset_thread_list = [] for i, env_id in enumerate(reset_env_list): # set seed if self._env_seed[env_id] is not None: try: if self._env_dynamic_seed is not None: self._pipe_parents[env_id].send(['seed', [self._env_seed[env_id], self._env_dynamic_seed], {}]) else: self._pipe_parents[env_id].send(['seed', [self._env_seed[env_id]], {}]) ret = self._pipe_parents[env_id].recv() self._check_data({env_id: ret}) self._env_seed[env_id] = None # seed only use once except BaseException as e: logging.warning( "subprocess reset set seed failed, ignore and continue... \n subprocess exception traceback: \n" + traceback.format_exc() ) self._env_states[env_id] = EnvState.RESET reset_thread = PropagatingThread(target=self._reset, args=(env_id, )) reset_thread.daemon = True reset_thread_list.append(reset_thread) for t in reset_thread_list: t.start() for t in reset_thread_list: t.join()
def _reset(self, env_id: int) -> None: def reset_fn(): if self._pipe_parents[env_id].poll(): recv_data = self._pipe_parents[env_id].recv() raise RuntimeError("unread data left before sending to the pipe: {}".format(repr(recv_data))) # if self._reset_param[env_id] is None, just reset specific env, not pass reset param if self._reset_param[env_id] is not None: assert isinstance(self._reset_param[env_id], dict), type(self._reset_param[env_id]) self._pipe_parents[env_id].send(['reset', [], self._reset_param[env_id]]) else: self._pipe_parents[env_id].send(['reset', [], None]) if not self._pipe_parents[env_id].poll(self._connect_timeout): raise ConnectionError("env reset connection timeout") # Leave it to try again obs = self._pipe_parents[env_id].recv() self._check_data({env_id: obs}, close=False) if self._shared_memory: obs = self._obs_buffers[env_id].get() # it is necessary to add lock for the updates of env_state with self._lock: self._env_states[env_id] = EnvState.RUN self._ready_obs[env_id] = obs exceptions = [] for _ in range(self._max_retry): try: reset_fn() return except BaseException as e: logging.info("subprocess exception traceback: \n" + traceback.format_exc()) if self._retry_type == 'renew' or isinstance(e, pickle.UnpicklingError): self._pipe_parents[env_id].close() if self._subprocesses[env_id].is_alive(): self._subprocesses[env_id].terminate() self._create_env_subprocess(env_id) exceptions.append(e) time.sleep(self._retry_waiting_time) logging.error("Env {} reset has exceeded max retries({})".format(env_id, self._max_retry)) runtime_error = RuntimeError( "Env {} reset has exceeded max retries({}), and the latest exception is: {}".format( env_id, self._max_retry, str(exceptions[-1]) ) ) runtime_error.__traceback__ = exceptions[-1].__traceback__ if self._closed: # exception cased by main thread closing parent_remote return else: self.close() raise runtime_error
[docs] def step(self, actions: Dict[int, Any]) -> Dict[int, namedtuple]: """ Overview: Step all environments. Reset an env if done. Arguments: - actions (:obj:`Dict[int, Any]`): {env_id: action} Returns: - timesteps (:obj:`Dict[int, namedtuple]`): {env_id: timestep}. Timestep is a \ ``BaseEnvTimestep`` tuple with observation, reward, done, env_info. Example: >>> actions_dict = {env_id: model.forward(obs) for env_id, obs in obs_dict.items())} >>> timesteps = env_manager.step(actions_dict): >>> for env_id, timestep in timesteps.items(): >>> pass .. note: - The env_id that appears in ``actions`` will also be returned in ``timesteps``. - Each environment is run by a subprocess separately. Once an environment is done, it is reset immediately. - Async subprocess env manager use ``connection.wait`` to poll. """ self._check_closed() env_ids = list(actions.keys()) assert all([self._env_states[env_id] == EnvState.RUN for env_id in env_ids] ), 'current env state are: {}, please check whether the requested env is in reset or done'.format( {env_id: self._env_states[env_id] for env_id in env_ids} ) for env_id, act in actions.items(): self._pipe_parents[env_id].send(['step', [act], None]) timesteps = {} step_args = self._async_args['step'] wait_num, timeout = min(step_args['wait_num'], len(env_ids)), step_args['timeout'] rest_env_ids = list(set(env_ids).union(self._waiting_env['step'])) ready_env_ids = [] cur_rest_env_ids = copy.deepcopy(rest_env_ids) while True: rest_conn = [self._pipe_parents[env_id] for env_id in cur_rest_env_ids] ready_conn, ready_ids = AsyncSubprocessEnvManager.wait(rest_conn, min(wait_num, len(rest_conn)), timeout) cur_ready_env_ids = [cur_rest_env_ids[env_id] for env_id in ready_ids] assert len(cur_ready_env_ids) == len(ready_conn) # timesteps.update({env_id: p.recv() for env_id, p in zip(cur_ready_env_ids, ready_conn)}) for env_id, p in zip(cur_ready_env_ids, ready_conn): try: timesteps.update({env_id: p.recv()}) except pickle.UnpicklingError as e: timestep = BaseEnvTimestep(None, None, None, {'abnormal': True}) timesteps.update({env_id: timestep}) self._pipe_parents[env_id].close() if self._subprocesses[env_id].is_alive(): self._subprocesses[env_id].terminate() self._create_env_subprocess(env_id) self._check_data(timesteps) ready_env_ids += cur_ready_env_ids cur_rest_env_ids = list(set(cur_rest_env_ids).difference(set(cur_ready_env_ids))) # At least one not done env timestep, or all envs' steps are finished if any([not t.done for t in timesteps.values()]) or len(ready_conn) == len(rest_conn): break self._waiting_env['step']: set for env_id in rest_env_ids: if env_id in ready_env_ids: if env_id in self._waiting_env['step']: self._waiting_env['step'].remove(env_id) else: self._waiting_env['step'].add(env_id) if self._shared_memory: for i, (env_id, timestep) in enumerate(timesteps.items()): timesteps[env_id] = timestep._replace(obs=self._obs_buffers[env_id].get()) for env_id, timestep in timesteps.items(): if is_abnormal_timestep(timestep): self._env_states[env_id] = EnvState.ERROR continue if timestep.done: self._env_episode_count[env_id] += 1 if self._env_episode_count[env_id] < self._episode_num: if self._auto_reset: if self._reset_inplace: # reset in subprocess at once self._env_states[env_id] = EnvState.RUN self._ready_obs[env_id] = timestep.obs else: # in this case, ready_obs is updated in ``self._reset`` self._env_states[env_id] = EnvState.RESET reset_thread = PropagatingThread(target=self._reset, args=(env_id, ), name='regular_reset') reset_thread.daemon = True reset_thread.start() else: # in the case that auto_reset=False, caller should call ``env_manager.reset`` manually self._env_states[env_id] = EnvState.NEED_RESET else: self._env_states[env_id] = EnvState.DONE else: self._ready_obs[env_id] = timestep.obs return timesteps
# This method must be staticmethod, otherwise there will be some resource conflicts(e.g. port or file) # Env must be created in worker, which is a trick of avoiding env pickle errors. # A more robust version is used by default. But this one is also preserved.
[docs] @staticmethod def worker_fn( p: connection.Connection, c: connection.Connection, env_fn_wrapper: 'CloudPickleWrapper', obs_buffer: ShmBuffer, method_name_list: list, reset_inplace: bool = False, ) -> None: # noqa """ Overview: Subprocess's target function to run. """ torch.set_num_threads(1) env_fn = env_fn_wrapper.data env = env_fn() p.close() try: while True: try: cmd, args, kwargs = c.recv() except EOFError: # for the case when the pipe has been closed c.close() break try: if cmd == 'getattr': ret = getattr(env, args[0]) elif cmd in method_name_list: if cmd == 'step': timestep = env.step(*args, **kwargs) if is_abnormal_timestep(timestep): ret = timestep else: if reset_inplace and timestep.done: obs = env.reset() timestep = timestep._replace(obs=obs) if obs_buffer is not None: obs_buffer.fill(timestep.obs) timestep = timestep._replace(obs=None) ret = timestep elif cmd == 'reset': ret = env.reset(*args, **kwargs) # obs if obs_buffer is not None: obs_buffer.fill(ret) ret = None elif args is None and kwargs is None: ret = getattr(env, cmd)() else: ret = getattr(env, cmd)(*args, **kwargs) else: raise KeyError("not support env cmd: {}".format(cmd)) c.send(ret) except Exception as e: # when there are some errors in env, worker_fn will send the errors to env manager # directly send error to another process will lose the stack trace, so we create a new Exception logging.warning("subprocess exception traceback: \n" + traceback.format_exc()) c.send( e.__class__( '\nEnv Process Exception:\n' + ''.join(traceback.format_tb(e.__traceback__)) + repr(e) ) ) if cmd == 'close': c.close() break except KeyboardInterrupt: c.close()
[docs] @staticmethod def worker_fn_robust( parent, child, env_fn_wrapper, obs_buffer, method_name_list, reset_timeout=None, step_timeout=None, reset_inplace=False, ) -> None: """ Overview: A more robust version of subprocess's target function to run. Used by default. """ torch.set_num_threads(1) env_fn = env_fn_wrapper.data env = env_fn() parent.close() @timeout_wrapper(timeout=step_timeout) def step_fn(*args, **kwargs): timestep = env.step(*args, **kwargs) if is_abnormal_timestep(timestep): ret = timestep else: if reset_inplace and timestep.done: obs = env.reset() timestep = timestep._replace(obs=obs) if obs_buffer is not None: obs_buffer.fill(timestep.obs) timestep = timestep._replace(obs=None) ret = timestep return ret @timeout_wrapper(timeout=reset_timeout) def reset_fn(*args, **kwargs): try: ret = env.reset(*args, **kwargs) if obs_buffer is not None: obs_buffer.fill(ret) ret = None return ret except BaseException as e: logging.warning("subprocess exception traceback: \n" + traceback.format_exc()) env.close() raise e while True: try: cmd, args, kwargs = child.recv() except EOFError: # for the case when the pipe has been closed child.close() break try: if cmd == 'getattr': ret = getattr(env, args[0]) elif cmd in method_name_list: if cmd == 'step': ret = step_fn(*args) elif cmd == 'reset': if kwargs is None: kwargs = {} ret = reset_fn(*args, **kwargs) elif cmd == 'render': from ding.utils import render ret = render(env, **kwargs) elif args is None and kwargs is None: ret = getattr(env, cmd)() else: ret = getattr(env, cmd)(*args, **kwargs) else: raise KeyError("not support env cmd: {}".format(cmd)) child.send(ret) except BaseException as e: logging.debug("Sub env '{}' error when executing {}".format(str(env), cmd)) # when there are some errors in env, worker_fn will send the errors to env manager # directly send error to another process will lose the stack trace, so we create a new Exception logging.warning("subprocess exception traceback: \n" + traceback.format_exc()) child.send( e.__class__('\nEnv Process Exception:\n' + ''.join(traceback.format_tb(e.__traceback__)) + repr(e)) ) if cmd == 'close': child.close() break
def _check_data(self, data: Dict, close: bool = True) -> None: exceptions = [] for i, d in data.items(): if isinstance(d, BaseException): self._env_states[i] = EnvState.ERROR exceptions.append(d) # when receiving env Exception, env manager will safely close and raise this Exception to caller if len(exceptions) > 0: if close: self.close() raise exceptions[0] # override def __getattr__(self, key: str) -> Any: self._check_closed() # we suppose that all the envs has the same attributes, if you need different envs, please # create different env managers. if not hasattr(self._env_ref, key): raise AttributeError("env `{}` doesn't have the attribute `{}`".format(type(self._env_ref), key)) if isinstance(getattr(self._env_ref, key), MethodType) and key not in self.method_name_list: raise RuntimeError("env getattr doesn't supports method({}), please override method_name_list".format(key)) for _, p in self._pipe_parents.items(): p.send(['getattr', [key], {}]) data = {i: p.recv() for i, p in self._pipe_parents.items()} self._check_data(data) ret = [data[i] for i in self._pipe_parents.keys()] return ret # override
[docs] def enable_save_replay(self, replay_path: Union[List[str], str]) -> None: """ Overview: Set each env's replay save path. Arguments: - replay_path (:obj:`Union[List[str], str]`): List of paths for each environment; \ Or one path for all environments. """ if isinstance(replay_path, str): replay_path = [replay_path] * self.env_num self._env_replay_path = replay_path
# override
[docs] def close(self) -> None: """ Overview: CLose the env manager and release all related resources. """ if self._closed: return self._closed = True for _, p in self._pipe_parents.items(): p.send(['close', None, None]) for env_id, p in self._pipe_parents.items(): if not p.poll(5): continue p.recv() for i in range(self._env_num): self._env_states[i] = EnvState.VOID # disable process join for avoiding hang # for p in self._subprocesses: # p.join() for _, p in self._subprocesses.items(): p.terminate() for _, p in self._pipe_parents.items(): p.close()
@staticmethod def wait(rest_conn: list, wait_num: int, timeout: Optional[float] = None) -> Tuple[list, list]: """ Overview: Wait at least enough(len(ready_conn) >= wait_num) connections within timeout constraint. If timeout is None and wait_num == len(ready_conn), means sync mode; If timeout is not None, will return when len(ready_conn) >= wait_num and this method takes more than timeout seconds. """ assert 1 <= wait_num <= len(rest_conn ), 'please indicate proper wait_num: <wait_num: {}, rest_conn_num: {}>'.format( wait_num, len(rest_conn) ) rest_conn_set = set(rest_conn) ready_conn = set() start_time = time.time() while len(rest_conn_set) > 0: if len(ready_conn) >= wait_num and timeout: if (time.time() - start_time) >= timeout: break finish_conn = set(connection.wait(rest_conn_set, timeout=timeout)) ready_conn = ready_conn.union(finish_conn) rest_conn_set = rest_conn_set.difference(finish_conn) ready_ids = [rest_conn.index(c) for c in ready_conn] return list(ready_conn), ready_ids
[docs]@ENV_MANAGER_REGISTRY.register('subprocess') class SyncSubprocessEnvManager(AsyncSubprocessEnvManager): config = dict( episode_num=float("inf"), max_retry=1, step_timeout=None, auto_reset=True, reset_timeout=None, retry_type='reset', retry_waiting_time=0.1, # subprocess specified args shared_memory=True, copy_on_get=True, context='spawn' if platform.system().lower() == 'windows' else 'fork', wait_num=float("inf"), # inf mean all the environments step_wait_timeout=None, connect_timeout=60, reset_inplace=False, # if reset_inplace=True in SyncSubprocessEnvManager, the interaction can be reproducible. )
[docs] def step(self, actions: Dict[int, Any]) -> Dict[int, namedtuple]: """ Overview: Step all environments. Reset an env if done. Arguments: - actions (:obj:`Dict[int, Any]`): {env_id: action} Returns: - timesteps (:obj:`Dict[int, namedtuple]`): {env_id: timestep}. Timestep is a \ ``BaseEnvTimestep`` tuple with observation, reward, done, env_info. Example: >>> actions_dict = {env_id: model.forward(obs) for env_id, obs in obs_dict.items())} >>> timesteps = env_manager.step(actions_dict): >>> for env_id, timestep in timesteps.items(): >>> pass .. note:: - The env_id that appears in ``actions`` will also be returned in ``timesteps``. - Each environment is run by a subprocess separately. Once an environment is done, it is reset immediately. """ self._check_closed() env_ids = list(actions.keys()) assert all([self._env_states[env_id] == EnvState.RUN for env_id in env_ids] ), 'current env state are: {}, please check whether the requested env is in reset or done'.format( {env_id: self._env_states[env_id] for env_id in env_ids} ) for env_id, act in actions.items(): # it is necessary to set kwargs as None for saving cost of serialization in some env like cartpole, # and step method never uses kwargs in known envs. self._pipe_parents[env_id].send(['step', [act], None]) # === This part is different from async one. === # === Because operate in this way is more efficient. === timesteps = {} ready_conn = [self._pipe_parents[env_id] for env_id in env_ids] # timesteps.update({env_id: p.recv() for env_id, p in zip(env_ids, ready_conn)}) for env_id, p in zip(env_ids, ready_conn): try: timesteps.update({env_id: p.recv()}) except pickle.UnpicklingError as e: timestep = BaseEnvTimestep(None, None, None, {'abnormal': True}) timesteps.update({env_id: timestep}) self._pipe_parents[env_id].close() if self._subprocesses[env_id].is_alive(): self._subprocesses[env_id].terminate() self._create_env_subprocess(env_id) self._check_data(timesteps) # ====================================================== if self._shared_memory: # TODO(nyz) optimize sync shm for i, (env_id, timestep) in enumerate(timesteps.items()): timesteps[env_id] = timestep._replace(obs=self._obs_buffers[env_id].get()) for env_id, timestep in timesteps.items(): if is_abnormal_timestep(timestep): self._env_states[env_id] = EnvState.ERROR continue if timestep.done: self._env_episode_count[env_id] += 1 if self._env_episode_count[env_id] < self._episode_num: if self._auto_reset: if self._reset_inplace: # reset in subprocess at once self._env_states[env_id] = EnvState.RUN self._ready_obs[env_id] = timestep.obs else: # in this case, ready_obs is updated in ``self._reset`` self._env_states[env_id] = EnvState.RESET reset_thread = PropagatingThread(target=self._reset, args=(env_id, ), name='regular_reset') reset_thread.daemon = True reset_thread.start() else: # in the case that auto_reset=False, caller should call ``env_manager.reset`` manually self._env_states[env_id] = EnvState.NEED_RESET else: self._env_states[env_id] = EnvState.DONE else: self._ready_obs[env_id] = timestep.obs return timesteps
[docs]@ENV_MANAGER_REGISTRY.register('subprocess_v2') class SubprocessEnvManagerV2(SyncSubprocessEnvManager): """ Overview: SyncSubprocessEnvManager for new task pipeline and interfaces coupled with treetensor. """ @property def ready_obs(self) -> tnp.array: """ Overview: Get the ready (next) observation in ``tnp.array`` type, which is uniform for both async/sync scenarios. Return: - ready_obs (:obj:`tnp.array`): A stacked treenumpy-type observation data. Example: >>> obs = env_manager.ready_obs >>> action = model(obs) # model input np obs and output np action >>> timesteps = env_manager.step(action) """ no_done_env_idx = [i for i, s in self._env_states.items() if s != EnvState.DONE] sleep_count = 0 while not any([self._env_states[i] == EnvState.RUN for i in no_done_env_idx]): if sleep_count != 0 and sleep_count % 10000 == 0: logging.warning( 'VEC_ENV_MANAGER: all the not done envs are resetting, sleep {} times'.format(sleep_count) ) time.sleep(0.001) sleep_count += 1 return tnp.stack([tnp.array(self._ready_obs[i]) for i in self.ready_env])
[docs] def step(self, actions: Union[List[tnp.ndarray], tnp.ndarray]) -> List[tnp.ndarray]: """ Overview: Execute env step according to input actions. And reset an env if done. Arguments: - actions (:obj:`Union[List[tnp.ndarray], tnp.ndarray]`): actions came from outer caller like policy. Returns: - timesteps (:obj:`List[tnp.ndarray]`): Each timestep is a tnp.array with observation, reward, done, \ info, env_id. """ if isinstance(actions, tnp.ndarray): # zip operation will lead to wrong behaviour if not split data split_action = tnp.split(actions, actions.shape[0]) split_action = [s.squeeze(0) for s in split_action] else: split_action = actions actions = {env_id: a for env_id, a in zip(self.ready_obs_id, split_action)} timesteps = super().step(actions) new_data = [] for env_id, timestep in timesteps.items(): obs, reward, done, info = timestep # make the type and content of key as similar as identifier, # in order to call them as attribute (e.g. timestep.xxx), such as ``TimeLimit.truncated`` in cartpole info info = make_key_as_identifier(info) info = remove_illegal_item(info) new_data.append(tnp.array({'obs': obs, 'reward': reward, 'done': done, 'info': info, 'env_id': env_id})) return new_data