`ding.worker.collector.episode_serial_collector`¶

`ding.worker.collector.episode_serial_collector` ¶

`EpisodeSerialCollector` ¶

Bases: ISerialCollector

Overview

Episode collector(n_episode)

Interfaces: init, reset, reset_env, reset_policy, collect, close Property: envstep

`envstep` `property` `writable` ¶

Overview

Print the total envstep count.

Return: - envstep (:obj:int): The total envstep count.

`init(cfg, env=None, policy=None, tb_logger=None, exp_name='default_experiment', instance_name='collector')` ¶

Overview

Initialization method.

Arguments: - cfg (:obj:EasyDict): Config dict - env (:obj:BaseEnvManager): the subclass of vectorized env_manager(BaseEnvManager) - policy (:obj:namedtuple): the api namedtuple of collect_mode policy - tb_logger (:obj:SummaryWriter): tensorboard handle

`reset_env(_env=None)` ¶

Overview

Reset the environment. If _env is None, reset the old environment. If _env is not None, replace the old environment in the collector with the new passed in environment and launch.

Arguments: - env (:obj:Optional[BaseEnvManager]): instance of the subclass of vectorized env_manager(BaseEnvManager)

`reset_policy(_policy=None)` ¶

Overview

Reset the policy. If _policy is None, reset the old policy. If _policy is not None, replace the old policy in the collector with the new passed in policy.

Arguments: - policy (:obj:Optional[namedtuple]): the api namedtuple of collect_mode policy

`reset(_policy=None, _env=None)` ¶

Overview

Reset the environment and policy. If _env is None, reset the old environment. If _env is not None, replace the old environment in the collector with the new passed in environment and launch. If _policy is None, reset the old policy. If _policy is not None, replace the old policy in the collector with the new passed in policy.

Arguments: - policy (:obj:Optional[namedtuple]): the api namedtuple of collect_mode policy - env (:obj:Optional[BaseEnvManager]): instance of the subclass of vectorized env_manager(BaseEnvManager)

`close()` ¶

Overview

Close the collector. If end_flag is False, close the environment, flush the tb_logger and close the tb_logger.

`del()` ¶

Overview

Execute the close command and close the collector. del is automatically called to destroy the collector instance when the collector finishes its work

`collect(n_episode=None, train_iter=0, policy_kwargs=None)` ¶

Overview

Collect n_episode data with policy_kwargs, which is already trained train_iter iterations

Arguments: - n_episode (:obj:int): the number of collecting data episode - train_iter (:obj:int): the number of training iteration - policy_kwargs (:obj:dict): the keyword args for policy forward Returns: - return_data (:obj:List): A list containing collected episodes if not get_train_sample, otherwise, return train_samples split by unroll_len.

Full Source Code

../ding/worker/collector/episode_serial_collector.py

from typing import Optional, Any, Listfrom collections import namedtuplefrom easydict import EasyDictimport numpy as npimport torchfrom ding.envs import BaseEnvManagerfrom ding.utils import build_logger, EasyTimer, SERIAL_COLLECTOR_REGISTRYfrom ding.torch_utils import to_tensor, to_ndarrayfrom .base_serial_collector import ISerialCollector, CachePool, TrajBuffer, INF, to_tensor_transitions@SERIAL_COLLECTOR_REGISTRY.register('episode')class EpisodeSerialCollector(ISerialCollector):    """    Overview:        Episode collector(n_episode)    Interfaces:        __init__, reset, reset_env, reset_policy, collect, close    Property:        envstep    """    config = dict(        deepcopy_obs=False, transform_obs=False, collect_print_freq=100, get_train_sample=False, reward_shaping=False    )    def __init__(            self,            cfg: EasyDict,            env: BaseEnvManager = None,            policy: namedtuple = None,            tb_logger: 'SummaryWriter' = None,  # noqa            exp_name: Optional[str] = 'default_experiment',            instance_name: Optional[str] = 'collector'    ) -> None:        """        Overview:            Initialization method.        Arguments:            - cfg (:obj:`EasyDict`): Config dict            - env (:obj:`BaseEnvManager`): the subclass of vectorized env_manager(BaseEnvManager)            - policy (:obj:`namedtuple`): the api namedtuple of collect_mode policy            - tb_logger (:obj:`SummaryWriter`): tensorboard handle        """        self._exp_name = exp_name        self._instance_name = instance_name        self._collect_print_freq = cfg.collect_print_freq        self._deepcopy_obs = cfg.deepcopy_obs        self._transform_obs = cfg.transform_obs        self._cfg = cfg        self._timer = EasyTimer()        self._end_flag = False        if tb_logger is not None:            self._logger, _ = build_logger(                path='./{}/log/{}'.format(self._exp_name, self._instance_name), name=self._instance_name, need_tb=False            )            self._tb_logger = tb_logger        else:            self._logger, self._tb_logger = build_logger(                path='./{}/log/{}'.format(self._exp_name, self._instance_name), name=self._instance_name            )        self.reset(policy, env)    def reset_env(self, _env: Optional[BaseEnvManager] = None) -> None:        """        Overview:            Reset the environment.            If _env is None, reset the old environment.            If _env is not None, replace the old environment in the collector with the new passed \                in environment and launch.        Arguments:            - env (:obj:`Optional[BaseEnvManager]`): instance of the subclass of vectorized \                env_manager(BaseEnvManager)        """        if _env is not None:            self._env = _env            self._env.launch()            self._env_num = self._env.env_num        else:            self._env.reset()    def reset_policy(self, _policy: Optional[namedtuple] = None) -> None:        """        Overview:            Reset the policy.            If _policy is None, reset the old policy.            If _policy is not None, replace the old policy in the collector with the new passed in policy.        Arguments:            - policy (:obj:`Optional[namedtuple]`): the api namedtuple of collect_mode policy        """        assert hasattr(self, '_env'), "please set env first"        if _policy is not None:            self._policy = _policy            self._policy_cfg = self._policy.get_attribute('cfg')            self._default_n_episode = _policy.get_attribute('n_episode')            self._unroll_len = _policy.get_attribute('unroll_len')            self._on_policy = _policy.get_attribute('on_policy')            self._traj_len = INF            self._logger.debug(                'Set default n_episode mode(n_episode({}), env_num({}), traj_len({}))'.format(                    self._default_n_episode, self._env_num, self._traj_len                )            )        self._policy.reset()    def reset(self, _policy: Optional[namedtuple] = None, _env: Optional[BaseEnvManager] = None) -> None:        """        Overview:            Reset the environment and policy.            If _env is None, reset the old environment.            If _env is not None, replace the old environment in the collector with the new passed \                in environment and launch.            If _policy is None, reset the old policy.            If _policy is not None, replace the old policy in the collector with the new passed in policy.        Arguments:            - policy (:obj:`Optional[namedtuple]`): the api namedtuple of collect_mode policy            - env (:obj:`Optional[BaseEnvManager]`): instance of the subclass of vectorized \                env_manager(BaseEnvManager)        """        if _env is not None:            self.reset_env(_env)        if _policy is not None:            self.reset_policy(_policy)        self._obs_pool = CachePool('obs', self._env_num, deepcopy=self._deepcopy_obs)        self._policy_output_pool = CachePool('policy_output', self._env_num)        # _traj_buffer is {env_id: TrajBuffer}, is used to store traj_len pieces of transitions        self._traj_buffer = {env_id: TrajBuffer(maxlen=self._traj_len) for env_id in range(self._env_num)}        self._env_info = {env_id: {'time': 0., 'step': 0} for env_id in range(self._env_num)}        self._episode_info = []        self._total_envstep_count = 0        self._total_episode_count = 0        self._total_duration = 0        self._last_train_iter = 0        self._end_flag = False    def _reset_stat(self, env_id: int) -> None:        """        Overview:            Reset the collector's state. Including reset the traj_buffer, obs_pool, policy_output_pool\                and env_info. Reset these states according to env_id. You can refer to base_serial_collector\                to get more messages.        Arguments:            - env_id (:obj:`int`): the id where we need to reset the collector's state        """        self._traj_buffer[env_id].clear()        self._obs_pool.reset(env_id)        self._policy_output_pool.reset(env_id)        self._env_info[env_id] = {'time': 0., 'step': 0}    @property    def envstep(self) -> int:        """        Overview:            Print the total envstep count.        Return:            - envstep (:obj:`int`): The total envstep count.        """        return self._total_envstep_count    @envstep.setter    def envstep(self, value: int) -> None:        """        Overview:            Set the total envstep count.        Arguments:            - value (:obj:`int`): The total envstep count.        """        self._total_envstep_count = value    def close(self) -> None:        """        Overview:            Close the collector. If end_flag is False, close the environment, flush the tb_logger\                and close the tb_logger.        """        if self._end_flag:            return        self._end_flag = True        self._env.close()        self._tb_logger.flush()        self._tb_logger.close()    def __del__(self) -> None:        """        Overview:            Execute the close command and close the collector. __del__ is automatically called to \                destroy the collector instance when the collector finishes its work        """        self.close()    def collect(self,                n_episode: Optional[int] = None,                train_iter: int = 0,                policy_kwargs: Optional[dict] = None) -> List[Any]:        """        Overview:            Collect `n_episode` data with policy_kwargs, which is already trained `train_iter` iterations        Arguments:            - n_episode (:obj:`int`): the number of collecting data episode            - train_iter (:obj:`int`): the number of training iteration            - policy_kwargs (:obj:`dict`): the keyword args for policy forward        Returns:            - return_data (:obj:`List`): A list containing collected episodes if not get_train_sample, otherwise, \                return train_samples split by unroll_len.        """        if n_episode is None:            if self._default_n_episode is None:                raise RuntimeError("Please specify collect n_episode")            else:                n_episode = self._default_n_episode        assert n_episode >= self._env_num, "Please make sure n_episode >= env_num{}/{}".format(n_episode, self._env_num)        if policy_kwargs is None:            policy_kwargs = {}        collected_episode = 0        return_data = []        ready_env_id = set()        remain_episode = n_episode        while True:            with self._timer:                # Get current env obs.                obs = self._env.ready_obs                new_available_env_id = set(obs.keys()).difference(ready_env_id)                ready_env_id = ready_env_id.union(set(list(new_available_env_id)[:remain_episode]))                remain_episode -= min(len(new_available_env_id), remain_episode)                obs = {env_id: obs[env_id] for env_id in ready_env_id}                # Policy forward.                self._obs_pool.update(obs)                if self._transform_obs:                    obs = to_tensor(obs, dtype=torch.float32)                policy_output = self._policy.forward(obs, **policy_kwargs)                self._policy_output_pool.update(policy_output)                # Interact with env.                actions = {env_id: output['action'] for env_id, output in policy_output.items()}                actions = to_ndarray(actions)                timesteps = self._env.step(actions)            # TODO(nyz) this duration may be inaccurate in async env            interaction_duration = self._timer.value / len(timesteps)            # TODO(nyz) vectorize this for loop            for env_id, timestep in timesteps.items():                with self._timer:                    if timestep.info.get('abnormal', False):                        # If there is an abnormal timestep, reset all the related variables(including this env).                        # suppose there is no reset param, just reset this env                        self._env.reset({env_id: None})                        self._policy.reset([env_id])                        self._reset_stat(env_id)                        self._logger.info('Env{} returns a abnormal step, its info is {}'.format(env_id, timestep.info))                        continue                    transition = self._policy.process_transition(                        self._obs_pool[env_id], self._policy_output_pool[env_id], timestep                    )                    # ``train_iter`` passed in from ``serial_entry``, indicates current collecting model's iteration.                    transition['collect_iter'] = train_iter                    self._traj_buffer[env_id].append(transition)                    self._env_info[env_id]['step'] += 1                    self._total_envstep_count += 1                    # prepare data                    if timestep.done:                        transitions = to_tensor_transitions(self._traj_buffer[env_id], not self._deepcopy_obs)                        if self._cfg.reward_shaping:                            self._env.reward_shaping(env_id, transitions)                        if self._cfg.get_train_sample:                            train_sample = self._policy.get_train_sample(transitions)                            return_data.extend(train_sample)                        else:                            return_data.append(transitions)                        self._traj_buffer[env_id].clear()                self._env_info[env_id]['time'] += self._timer.value + interaction_duration                # If env is done, record episode info and reset                if timestep.done:                    self._total_episode_count += 1                    reward = timestep.info['eval_episode_return']                    info = {                        'reward': reward,                        'time': self._env_info[env_id]['time'],                        'step': self._env_info[env_id]['step'],                    }                    collected_episode += 1                    self._episode_info.append(info)                    self._policy.reset([env_id])                    self._reset_stat(env_id)                    ready_env_id.remove(env_id)            if collected_episode >= n_episode:                break        # log        self._output_log(train_iter)        return return_data    def _output_log(self, train_iter: int) -> None:        """        Overview:            Print the output log information. You can refer to Docs/Best Practice/How to understand\             training generated folders/Serial mode/log/collector for more details.        Arguments:            - train_iter (:obj:`int`): the number of training iteration.        """        if (train_iter - self._last_train_iter) >= self._collect_print_freq and len(self._episode_info) > 0:            self._last_train_iter = train_iter            episode_count = len(self._episode_info)            envstep_count = sum([d['step'] for d in self._episode_info])            duration = sum([d['time'] for d in self._episode_info])            episode_return = [d['reward'] for d in self._episode_info]            self._total_duration += duration            info = {                'episode_count': episode_count,                'envstep_count': envstep_count,                'avg_envstep_per_episode': envstep_count / episode_count,                'avg_envstep_per_sec': envstep_count / duration,                'avg_episode_per_sec': episode_count / duration,                'collect_time': duration,                'reward_mean': np.mean(episode_return),                'reward_std': np.std(episode_return),                'reward_max': np.max(episode_return),                'reward_min': np.min(episode_return),                'total_envstep_count': self._total_envstep_count,                'total_episode_count': self._total_episode_count,                'total_duration': self._total_duration,                # 'each_reward': episode_return,            }            self._episode_info.clear()            self._logger.info("collect end:\n{}".format('\n'.join(['{}: {}'.format(k, v) for k, v in info.items()])))            for k, v in info.items():                if k in ['each_reward']:                    continue                self._tb_logger.add_scalar('{}_iter/'.format(self._instance_name) + k, v, train_iter)                if k in ['total_envstep_count']:                    continue                self._tb_logger.add_scalar('{}_step/'.format(self._instance_name) + k, v, self._total_envstep_count)

ding.worker.collector.episode_serial_collector¶