`ding.worker.collector.battle_sample_serial_collector`¶

`ding.worker.collector.battle_sample_serial_collector` ¶

`BattleSampleSerialCollector` ¶

Bases: ISerialCollector

Overview

Sample collector(n_sample) with multiple(n VS n) policy battle

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:List[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[List[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[List[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_sample=None, train_iter=0, drop_extra=True, policy_kwargs=None)` ¶

Overview

Collect n_sample data with policy_kwargs, which is already trained train_iter iterations.

Arguments: - n_sample (:obj:int): The number of collecting data sample. - train_iter (:obj:int): The number of training iteration when calling collect method. - drop_extra (:obj:bool): Whether to drop extra return_data more than n_sample. - policy_kwargs (:obj:dict): The keyword args for policy forward. Returns: - return_data (:obj:List): A list containing training samples.

Full Source Code

../ding/worker/collector/battle_sample_serial_collector.py

from typing import Optional, Any, List, Tuplefrom collections import namedtuplefrom easydict import EasyDictimport numpy as npimport torchfrom ding.envs import BaseEnvManagerfrom ding.utils import build_logger, EasyTimer, SERIAL_COLLECTOR_REGISTRY, dicts_to_lists, one_time_warningfrom ding.torch_utils import to_tensor, to_ndarrayfrom .base_serial_collector import ISerialCollector, CachePool, TrajBuffer, INF, to_tensor_transitions@SERIAL_COLLECTOR_REGISTRY.register('sample_1v1')class BattleSampleSerialCollector(ISerialCollector):    """    Overview:        Sample collector(n_sample) with multiple(n VS n) policy battle    Interfaces:        __init__, reset, reset_env, reset_policy, collect, close    Property:        envstep    """    config = dict(deepcopy_obs=False, transform_obs=False, collect_print_freq=100)    def __init__(            self,            cfg: EasyDict,            env: BaseEnvManager = None,            policy: List[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:`List[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._traj_len = float("inf")        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[List[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[List[namedtuple]]`): the api namedtuple of collect_mode policy        """        assert hasattr(self, '_env'), "please set env first"        if _policy is not None:            assert len(_policy) > 1, "battle sample collector needs more than 1 policy, but found {}".format(                len(_policy)            )            self._policy = _policy            self._policy_num = len(self._policy)            self._default_n_sample = _policy[0].get_attribute('cfg').collect.get('n_sample', None)            self._unroll_len = _policy[0].get_attribute('unroll_len')            self._on_policy = _policy[0].get_attribute('cfg').on_policy            self._policy_collect_data = [                getattr(self._policy[i], 'collect_data', True) for i in range(self._policy_num)            ]            if self._default_n_sample is not None:                self._traj_len = max(                    self._unroll_len,                    self._default_n_sample // self._env_num + int(self._default_n_sample % self._env_num != 0)                )                self._logger.debug(                    'Set default n_sample mode(n_sample({}), env_num({}), traj_len({}))'.format(                        self._default_n_sample, self._env_num, self._traj_len                    )                )            else:                self._traj_len = INF        for p in self._policy:            p.reset()    def reset(self, _policy: Optional[List[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[List[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: {policy_id: TrajBuffer}}, is used to store traj_len pieces of transitions        self._traj_buffer = {            env_id: {policy_id: TrajBuffer(maxlen=self._traj_len)                     for policy_id in range(self._policy_num)}            for env_id in range(self._env_num)        }        self._env_info = {env_id: {'time': 0., 'step': 0, 'train_sample': 0} for env_id in range(self._env_num)}        self._episode_info = []        self._total_envstep_count = 0        self._total_episode_count = 0        self._total_train_sample_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        """        for i in range(2):            self._traj_buffer[env_id][i].clear()        self._obs_pool.reset(env_id)        self._policy_output_pool.reset(env_id)        self._env_info[env_id] = {'time': 0., 'step': 0, 'train_sample': 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_sample: Optional[int] = None,            train_iter: int = 0,            drop_extra: bool = True,            policy_kwargs: Optional[dict] = None    ) -> Tuple[List[Any], List[Any]]:        """        Overview:            Collect `n_sample` data with policy_kwargs, which is already trained `train_iter` iterations.        Arguments:            - n_sample (:obj:`int`): The number of collecting data sample.            - train_iter (:obj:`int`): The number of training iteration when calling collect method.            - drop_extra (:obj:`bool`): Whether to drop extra return_data more than `n_sample`.            - policy_kwargs (:obj:`dict`): The keyword args for policy forward.        Returns:            - return_data (:obj:`List`): A list containing training samples.        """        if n_sample is None:            if self._default_n_sample is None:                raise RuntimeError("Please specify collect n_sample")            else:                n_sample = self._default_n_sample        if n_sample % self._env_num != 0:            one_time_warning(                "Please make sure env_num is divisible by n_sample: {}/{}, ".format(n_sample, self._env_num) +                "which may cause convergence problems in a few algorithms"            )        if policy_kwargs is None:            policy_kwargs = {}        collected_sample = [0 for _ in range(self._policy_num)]        return_data = [[] for _ in range(self._policy_num)]        return_info = [[] for _ in range(self._policy_num)]        while any([c < n_sample for i, c in enumerate(collected_sample) if self._policy_collect_data[i]]):            with self._timer:                # Get current env obs.                obs = self._env.ready_obs                # Policy forward.                self._obs_pool.update(obs)                if self._transform_obs:                    obs = to_tensor(obs, dtype=torch.float32)                obs = dicts_to_lists(obs)                policy_output = [p.forward(obs[i], **policy_kwargs) for i, p in enumerate(self._policy)]                self._policy_output_pool.update(policy_output)                # Interact with env.                actions = {}                for policy_output_item in policy_output:                    for env_id, output in policy_output_item.items():                        if env_id not in actions:                            actions[env_id] = []                        actions[env_id].append(output['action'])                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():                self._env_info[env_id]['step'] += 1                self._total_envstep_count += 1                with self._timer:                    for policy_id, policy in enumerate(self._policy):                        if not self._policy_collect_data[policy_id]:                            continue                        policy_timestep_data = [d[policy_id] if not isinstance(d, bool) else d for d in timestep]                        policy_timestep = type(timestep)(*policy_timestep_data)                        transition = self._policy[policy_id].process_transition(                            self._obs_pool[env_id][policy_id], self._policy_output_pool[env_id][policy_id],                            policy_timestep                        )                        transition['collect_iter'] = train_iter                        self._traj_buffer[env_id][policy_id].append(transition)                        # prepare data                        if timestep.done or len(self._traj_buffer[env_id][policy_id]) == self._traj_len:                            transitions = to_tensor_transitions(                                self._traj_buffer[env_id][policy_id], not self._deepcopy_obs                            )                            train_sample = self._policy[policy_id].get_train_sample(transitions)                            return_data[policy_id].extend(train_sample)                            self._total_train_sample_count += len(train_sample)                            self._env_info[env_id]['train_sample'] += len(train_sample)                            collected_sample[policy_id] += len(train_sample)                            self._traj_buffer[env_id][policy_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                    info = {                        'time': self._env_info[env_id]['time'],                        'step': self._env_info[env_id]['step'],                        'train_sample': self._env_info[env_id]['train_sample'],                    }                    for i in range(self._policy_num):                        info['reward{}'.format(i)] = timestep.info[i]['eval_episode_return']                    self._episode_info.append(info)                    for i, p in enumerate(self._policy):                        p.reset([env_id])                    self._reset_stat(env_id)                    for policy_id in range(2):                        return_info[policy_id].append(timestep.info[policy_id])        # log        self._output_log(train_iter)        return_data = [r[:n_sample] for r in return_data]        if drop_extra:            return_data = return_data[:n_sample]        return return_data, return_info    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 = []            for i in range(self._policy_num):                episode_return_item = [d['reward{}'.format(i)] for d in self._episode_info]                episode_return.append(episode_return_item)            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,                'total_envstep_count': self._total_envstep_count,                'total_episode_count': self._total_episode_count,                'total_duration': self._total_duration,            }            for k, fn in {'mean': np.mean, 'std': np.std, 'max': np.max, 'min': np.min}.items():                for i in range(self._policy_num):                    # such as reward0_mean                    info['reward{}_{}'.format(i, k)] = fn(episode_return[i])            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():                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.battle_sample_serial_collector¶