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| import time | |
| from collections import deque, namedtuple | |
| from typing import Optional, Any, List | |
| import numpy as np | |
| import torch | |
| from ding.envs import BaseEnvManager | |
| from ding.torch_utils import to_ndarray | |
| from ding.utils import build_logger, EasyTimer, SERIAL_COLLECTOR_REGISTRY, one_time_warning, get_rank, get_world_size, \ | |
| broadcast_object_list, allreduce_data | |
| from ding.worker.collector.base_serial_collector import ISerialCollector | |
| from torch.nn import L1Loss | |
| from lzero.mcts.buffer.game_segment import GameSegment | |
| from lzero.mcts.utils import prepare_observation | |
| class MuZeroCollector(ISerialCollector): | |
| """ | |
| Overview: | |
| The Collector for MCTS+RL algorithms, including MuZero, EfficientZero, Sampled EfficientZero, Gumbel MuZero. | |
| Interfaces: | |
| __init__, reset, reset_env, reset_policy, _reset_stat, envstep, __del__, _compute_priorities, pad_and_save_last_trajectory, collect, _output_log, close | |
| Property: | |
| envstep | |
| """ | |
| # TO be compatible with ISerialCollector | |
| config = dict() | |
| def __init__( | |
| self, | |
| collect_print_freq: int = 100, | |
| env: BaseEnvManager = None, | |
| policy: namedtuple = None, | |
| tb_logger: 'SummaryWriter' = None, # noqa | |
| exp_name: Optional[str] = 'default_experiment', | |
| instance_name: Optional[str] = 'collector', | |
| policy_config: 'policy_config' = None, # noqa | |
| ) -> None: | |
| """ | |
| Overview: | |
| Init the collector according to input arguments. | |
| Arguments: | |
| - collect_print_freq (:obj:`int`): collect_print_frequency in terms of training_steps. | |
| - 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 | |
| - instance_name (:obj:`Optional[str]`): Name of this instance. | |
| - exp_name (:obj:`str`): Experiment name, which is used to indicate output directory. | |
| - policy_config: Config of game. | |
| """ | |
| self._exp_name = exp_name | |
| self._instance_name = instance_name | |
| self._collect_print_freq = collect_print_freq | |
| self._timer = EasyTimer() | |
| self._end_flag = False | |
| self._rank = get_rank() | |
| self._world_size = get_world_size() | |
| if self._rank == 0: | |
| 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 | |
| ) | |
| else: | |
| self._logger, _ = build_logger( | |
| path='./{}/log/{}'.format(self._exp_name, self._instance_name), name=self._instance_name, need_tb=False | |
| ) | |
| self._tb_logger = None | |
| self.policy_config = policy_config | |
| 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._default_n_episode = _policy.get_attribute('cfg').get('n_episode', None) | |
| self._logger.debug( | |
| 'Set default n_episode mode(n_episode({}), env_num({}))'.format(self._default_n_episode, self._env_num) | |
| ) | |
| 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._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 | |
| # A game_segment_pool implementation based on the deque structure. | |
| self.game_segment_pool = deque(maxlen=int(1e6)) | |
| self.unroll_plus_td_steps = self.policy_config.num_unroll_steps + self.policy_config.td_steps | |
| 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._env_info[env_id] = {'time': 0., 'step': 0} | |
| def envstep(self) -> int: | |
| """ | |
| Overview: | |
| Print the total envstep count. | |
| Return: | |
| - envstep (:obj:`int`): the total envstep count | |
| """ | |
| return self._total_envstep_count | |
| 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() | |
| if self._tb_logger: | |
| 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() | |
| # ============================================================== | |
| # MCTS+RL related core code | |
| # ============================================================== | |
| def _compute_priorities(self, i, pred_values_lst, search_values_lst): | |
| """ | |
| Overview: | |
| obtain the priorities at index i. | |
| Arguments: | |
| - i: index. | |
| - pred_values_lst: The list of value being predicted. | |
| - search_values_lst: The list of value obtained through search. | |
| """ | |
| if self.policy_config.use_priority: | |
| # Calculate priorities. The priorities are the L1 losses between the predicted | |
| # values and the search values. We use 'none' as the reduction parameter, which | |
| # means the loss is calculated for each element individually, instead of being summed or averaged. | |
| # A small constant (1e-6) is added to the results to avoid zero priorities. This | |
| # is done because zero priorities could potentially cause issues in some scenarios. | |
| pred_values = torch.from_numpy(np.array(pred_values_lst[i])).to(self.policy_config.device).float().view(-1) | |
| search_values = torch.from_numpy(np.array(search_values_lst[i])).to(self.policy_config.device | |
| ).float().view(-1) | |
| priorities = L1Loss(reduction='none' | |
| )(pred_values, | |
| search_values).detach().cpu().numpy() + 1e-6 | |
| else: | |
| # priorities is None -> use the max priority for all newly collected data | |
| priorities = None | |
| return priorities | |
| def pad_and_save_last_trajectory(self, i, last_game_segments, last_game_priorities, game_segments, done) -> None: | |
| """ | |
| Overview: | |
| put the last game segment into the pool if the current game is finished | |
| Arguments: | |
| - last_game_segments (:obj:`list`): list of the last game segments | |
| - last_game_priorities (:obj:`list`): list of the last game priorities | |
| - game_segments (:obj:`list`): list of the current game segments | |
| Note: | |
| (last_game_segments[i].obs_segment[-4:][j] == game_segments[i].obs_segment[:4][j]).all() is True | |
| """ | |
| # pad over last segment trajectory | |
| beg_index = self.policy_config.model.frame_stack_num | |
| end_index = beg_index + self.policy_config.num_unroll_steps | |
| # the start <frame_stack_num> obs is init zero obs, so we take the [<frame_stack_num> : <frame_stack_num>+<num_unroll_steps>] obs as the pad obs | |
| # e.g. the start 4 obs is init zero obs, the num_unroll_steps is 5, so we take the [4:9] obs as the pad obs | |
| pad_obs_lst = game_segments[i].obs_segment[beg_index:end_index] | |
| pad_child_visits_lst = game_segments[i].child_visit_segment[:self.policy_config.num_unroll_steps] | |
| # EfficientZero original repo bug: | |
| # pad_child_visits_lst = game_segments[i].child_visit_segment[beg_index:end_index] | |
| beg_index = 0 | |
| # self.unroll_plus_td_steps = self.policy_config.num_unroll_steps + self.policy_config.td_steps | |
| end_index = beg_index + self.unroll_plus_td_steps - 1 | |
| pad_reward_lst = game_segments[i].reward_segment[beg_index:end_index] | |
| if self.policy_config.use_ture_chance_label_in_chance_encoder: | |
| chance_lst = game_segments[i].chance_segment[beg_index:end_index] | |
| beg_index = 0 | |
| end_index = beg_index + self.unroll_plus_td_steps | |
| pad_root_values_lst = game_segments[i].root_value_segment[beg_index:end_index] | |
| if self.policy_config.gumbel_algo: | |
| pad_improved_policy_prob = game_segments[i].improved_policy_probs[beg_index:end_index] | |
| # pad over and save | |
| if self.policy_config.gumbel_algo: | |
| last_game_segments[i].pad_over(pad_obs_lst, pad_reward_lst, pad_root_values_lst, pad_child_visits_lst, next_segment_improved_policy = pad_improved_policy_prob) | |
| else: | |
| if self.policy_config.use_ture_chance_label_in_chance_encoder: | |
| last_game_segments[i].pad_over(pad_obs_lst, pad_reward_lst, pad_root_values_lst, pad_child_visits_lst, next_chances = chance_lst) | |
| else: | |
| last_game_segments[i].pad_over(pad_obs_lst, pad_reward_lst, pad_root_values_lst, pad_child_visits_lst) | |
| """ | |
| Note: | |
| game_segment element shape: | |
| obs: game_segment_length + stack + num_unroll_steps, 20+4 +5 | |
| rew: game_segment_length + stack + num_unroll_steps + td_steps -1 20 +5+3-1 | |
| action: game_segment_length -> 20 | |
| root_values: game_segment_length + num_unroll_steps + td_steps -> 20 +5+3 | |
| child_visits： game_segment_length + num_unroll_steps -> 20 +5 | |
| to_play: game_segment_length -> 20 | |
| action_mask: game_segment_length -> 20 | |
| """ | |
| last_game_segments[i].game_segment_to_array() | |
| # put the game segment into the pool | |
| self.game_segment_pool.append((last_game_segments[i], last_game_priorities[i], done[i])) | |
| # reset last game_segments | |
| last_game_segments[i] = None | |
| last_game_priorities[i] = None | |
| return None | |
| 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 game_segments | |
| """ | |
| 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 = {} | |
| temperature = policy_kwargs['temperature'] | |
| epsilon = policy_kwargs['epsilon'] | |
| collected_episode = 0 | |
| collected_step = 0 | |
| env_nums = self._env_num | |
| # initializations | |
| init_obs = self._env.ready_obs | |
| retry_waiting_time = 0.001 | |
| while len(init_obs.keys()) != self._env_num: | |
| # In order to be compatible with subprocess env_manager, in which sometimes self._env_num is not equal to | |
| # len(self._env.ready_obs), especially in tictactoe env. | |
| self._logger.info('The current init_obs.keys() is {}'.format(init_obs.keys())) | |
| self._logger.info('Before sleeping, the _env_states is {}'.format(self._env._env_states)) | |
| time.sleep(retry_waiting_time) | |
| self._logger.info('=' * 10 + 'Wait for all environments (subprocess) to finish resetting.' + '=' * 10) | |
| self._logger.info( | |
| 'After sleeping {}s, the current _env_states is {}'.format(retry_waiting_time, self._env._env_states) | |
| ) | |
| init_obs = self._env.ready_obs | |
| action_mask_dict = {i: to_ndarray(init_obs[i]['action_mask']) for i in range(env_nums)} | |
| to_play_dict = {i: to_ndarray(init_obs[i]['to_play']) for i in range(env_nums)} | |
| if self.policy_config.use_ture_chance_label_in_chance_encoder: | |
| chance_dict = {i: to_ndarray(init_obs[i]['chance']) for i in range(env_nums)} | |
| game_segments = [ | |
| GameSegment( | |
| self._env.action_space, | |
| game_segment_length=self.policy_config.game_segment_length, | |
| config=self.policy_config | |
| ) for _ in range(env_nums) | |
| ] | |
| # stacked observation windows in reset stage for init game_segments | |
| observation_window_stack = [[] for _ in range(env_nums)] | |
| for env_id in range(env_nums): | |
| observation_window_stack[env_id] = deque( | |
| [to_ndarray(init_obs[env_id]['observation']) for _ in range(self.policy_config.model.frame_stack_num)], | |
| maxlen=self.policy_config.model.frame_stack_num | |
| ) | |
| game_segments[env_id].reset(observation_window_stack[env_id]) | |
| dones = np.array([False for _ in range(env_nums)]) | |
| last_game_segments = [None for _ in range(env_nums)] | |
| last_game_priorities = [None for _ in range(env_nums)] | |
| # for priorities in self-play | |
| search_values_lst = [[] for _ in range(env_nums)] | |
| pred_values_lst = [[] for _ in range(env_nums)] | |
| if self.policy_config.gumbel_algo: | |
| improved_policy_lst = [[] for _ in range(env_nums)] | |
| # some logs | |
| eps_steps_lst, visit_entropies_lst = np.zeros(env_nums), np.zeros(env_nums) | |
| if self.policy_config.gumbel_algo: | |
| completed_value_lst = np.zeros(env_nums) | |
| self_play_moves = 0. | |
| self_play_episodes = 0. | |
| self_play_moves_max = 0 | |
| self_play_visit_entropy = [] | |
| total_transitions = 0 | |
| ready_env_id = set() | |
| remain_episode = n_episode | |
| while True: | |
| with self._timer: | |
| # Get current ready 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) | |
| stack_obs = {env_id: game_segments[env_id].get_obs() for env_id in ready_env_id} | |
| stack_obs = list(stack_obs.values()) | |
| action_mask_dict = {env_id: action_mask_dict[env_id] for env_id in ready_env_id} | |
| to_play_dict = {env_id: to_play_dict[env_id] for env_id in ready_env_id} | |
| action_mask = [action_mask_dict[env_id] for env_id in ready_env_id] | |
| to_play = [to_play_dict[env_id] for env_id in ready_env_id] | |
| if self.policy_config.use_ture_chance_label_in_chance_encoder: | |
| chance_dict = {env_id: chance_dict[env_id] for env_id in ready_env_id} | |
| chance = [chance_dict[env_id] for env_id in ready_env_id] | |
| stack_obs = to_ndarray(stack_obs) | |
| stack_obs = prepare_observation(stack_obs, self.policy_config.model.model_type) | |
| stack_obs = torch.from_numpy(stack_obs).to(self.policy_config.device).float() | |
| # ============================================================== | |
| # policy forward | |
| # ============================================================== | |
| policy_output = self._policy.forward(stack_obs, action_mask, temperature, to_play, epsilon) | |
| actions_no_env_id = {k: v['action'] for k, v in policy_output.items()} | |
| distributions_dict_no_env_id = {k: v['visit_count_distributions'] for k, v in policy_output.items()} | |
| if self.policy_config.sampled_algo: | |
| root_sampled_actions_dict_no_env_id = { | |
| k: v['root_sampled_actions'] | |
| for k, v in policy_output.items() | |
| } | |
| value_dict_no_env_id = {k: v['searched_value'] for k, v in policy_output.items()} | |
| pred_value_dict_no_env_id = {k: v['predicted_value'] for k, v in policy_output.items()} | |
| visit_entropy_dict_no_env_id = { | |
| k: v['visit_count_distribution_entropy'] | |
| for k, v in policy_output.items() | |
| } | |
| if self.policy_config.gumbel_algo: | |
| improved_policy_dict_no_env_id = {k: v['improved_policy_probs'] for k, v in policy_output.items()} | |
| completed_value_no_env_id = { | |
| k: v['roots_completed_value'] | |
| for k, v in policy_output.items() | |
| } | |
| # TODO(pu): subprocess | |
| actions = {} | |
| distributions_dict = {} | |
| if self.policy_config.sampled_algo: | |
| root_sampled_actions_dict = {} | |
| value_dict = {} | |
| pred_value_dict = {} | |
| visit_entropy_dict = {} | |
| if self.policy_config.gumbel_algo: | |
| improved_policy_dict = {} | |
| completed_value_dict = {} | |
| for index, env_id in enumerate(ready_env_id): | |
| actions[env_id] = actions_no_env_id.pop(index) | |
| distributions_dict[env_id] = distributions_dict_no_env_id.pop(index) | |
| if self.policy_config.sampled_algo: | |
| root_sampled_actions_dict[env_id] = root_sampled_actions_dict_no_env_id.pop(index) | |
| value_dict[env_id] = value_dict_no_env_id.pop(index) | |
| pred_value_dict[env_id] = pred_value_dict_no_env_id.pop(index) | |
| visit_entropy_dict[env_id] = visit_entropy_dict_no_env_id.pop(index) | |
| if self.policy_config.gumbel_algo: | |
| improved_policy_dict[env_id] = improved_policy_dict_no_env_id.pop(index) | |
| completed_value_dict[env_id] = completed_value_no_env_id.pop(index) | |
| # ============================================================== | |
| # Interact with env. | |
| # ============================================================== | |
| timesteps = self._env.step(actions) | |
| interaction_duration = self._timer.value / len(timesteps) | |
| 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 | |
| obs, reward, done, info = timestep.obs, timestep.reward, timestep.done, timestep.info | |
| if self.policy_config.sampled_algo: | |
| game_segments[env_id].store_search_stats( | |
| distributions_dict[env_id], value_dict[env_id], root_sampled_actions_dict[env_id] | |
| ) | |
| elif self.policy_config.gumbel_algo: | |
| game_segments[env_id].store_search_stats(distributions_dict[env_id], value_dict[env_id], improved_policy = improved_policy_dict[env_id]) | |
| else: | |
| game_segments[env_id].store_search_stats(distributions_dict[env_id], value_dict[env_id]) | |
| # append a transition tuple, including a_t, o_{t+1}, r_{t}, action_mask_{t}, to_play_{t} | |
| # in ``game_segments[env_id].init``, we have append o_{t} in ``self.obs_segment`` | |
| if self.policy_config.use_ture_chance_label_in_chance_encoder: | |
| game_segments[env_id].append( | |
| actions[env_id], to_ndarray(obs['observation']), reward, action_mask_dict[env_id], | |
| to_play_dict[env_id], chance_dict[env_id] | |
| ) | |
| else: | |
| game_segments[env_id].append( | |
| actions[env_id], to_ndarray(obs['observation']), reward, action_mask_dict[env_id], | |
| to_play_dict[env_id] | |
| ) | |
| # NOTE: the position of code snippet is very important. | |
| # the obs['action_mask'] and obs['to_play'] are corresponding to the next action | |
| action_mask_dict[env_id] = to_ndarray(obs['action_mask']) | |
| to_play_dict[env_id] = to_ndarray(obs['to_play']) | |
| if self.policy_config.use_ture_chance_label_in_chance_encoder: | |
| chance_dict[env_id] = to_ndarray(obs['chance']) | |
| if self.policy_config.ignore_done: | |
| dones[env_id] = False | |
| else: | |
| dones[env_id] = done | |
| visit_entropies_lst[env_id] += visit_entropy_dict[env_id] | |
| if self.policy_config.gumbel_algo: | |
| completed_value_lst[env_id] += np.mean(np.array(completed_value_dict[env_id])) | |
| eps_steps_lst[env_id] += 1 | |
| total_transitions += 1 | |
| if self.policy_config.use_priority: | |
| pred_values_lst[env_id].append(pred_value_dict[env_id]) | |
| search_values_lst[env_id].append(value_dict[env_id]) | |
| if self.policy_config.gumbel_algo: | |
| improved_policy_lst[env_id].append(improved_policy_dict[env_id]) | |
| # append the newest obs | |
| observation_window_stack[env_id].append(to_ndarray(obs['observation'])) | |
| # ============================================================== | |
| # we will save a game segment if it is the end of the game or the next game segment is finished. | |
| # ============================================================== | |
| # if game segment is full, we will save the last game segment | |
| if game_segments[env_id].is_full(): | |
| # pad over last segment trajectory | |
| if last_game_segments[env_id] is not None: | |
| # TODO(pu): return the one game segment | |
| self.pad_and_save_last_trajectory( | |
| env_id, last_game_segments, last_game_priorities, game_segments, dones | |
| ) | |
| # calculate priority | |
| priorities = self._compute_priorities(env_id, pred_values_lst, search_values_lst) | |
| pred_values_lst[env_id] = [] | |
| search_values_lst[env_id] = [] | |
| if self.policy_config.gumbel_algo: | |
| improved_policy_lst[env_id] = [] | |
| # the current game_segments become last_game_segment | |
| last_game_segments[env_id] = game_segments[env_id] | |
| last_game_priorities[env_id] = priorities | |
| # create new GameSegment | |
| game_segments[env_id] = GameSegment( | |
| self._env.action_space, | |
| game_segment_length=self.policy_config.game_segment_length, | |
| config=self.policy_config | |
| ) | |
| game_segments[env_id].reset(observation_window_stack[env_id]) | |
| self._env_info[env_id]['step'] += 1 | |
| collected_step += 1 | |
| self._env_info[env_id]['time'] += self._timer.value + interaction_duration | |
| 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'], | |
| 'visit_entropy': visit_entropies_lst[env_id] / eps_steps_lst[env_id], | |
| } | |
| if self.policy_config.gumbel_algo: | |
| info['completed_value'] = completed_value_lst[env_id] / eps_steps_lst[env_id] | |
| collected_episode += 1 | |
| self._episode_info.append(info) | |
| # ============================================================== | |
| # if it is the end of the game, we will save the game segment | |
| # ============================================================== | |
| # NOTE: put the penultimate game segment in one episode into the trajectory_pool | |
| # pad over 2th last game_segment using the last game_segment | |
| if last_game_segments[env_id] is not None: | |
| self.pad_and_save_last_trajectory( | |
| env_id, last_game_segments, last_game_priorities, game_segments, dones | |
| ) | |
| # store current segment trajectory | |
| priorities = self._compute_priorities(env_id, pred_values_lst, search_values_lst) | |
| # NOTE: put the last game segment in one episode into the trajectory_pool | |
| game_segments[env_id].game_segment_to_array() | |
| # assert len(game_segments[env_id]) == len(priorities) | |
| # NOTE: save the last game segment in one episode into the trajectory_pool if it's not null | |
| if len(game_segments[env_id].reward_segment) != 0: | |
| self.game_segment_pool.append((game_segments[env_id], priorities, dones[env_id])) | |
| # print(game_segments[env_id].reward_segment) | |
| # reset the finished env and init game_segments | |
| if n_episode > self._env_num: | |
| # Get current ready env obs. | |
| init_obs = self._env.ready_obs | |
| retry_waiting_time = 0.001 | |
| while len(init_obs.keys()) != self._env_num: | |
| # In order to be compatible with subprocess env_manager, in which sometimes self._env_num is not equal to | |
| # len(self._env.ready_obs), especially in tictactoe env. | |
| self._logger.info('The current init_obs.keys() is {}'.format(init_obs.keys())) | |
| self._logger.info('Before sleeping, the _env_states is {}'.format(self._env._env_states)) | |
| time.sleep(retry_waiting_time) | |
| self._logger.info( | |
| '=' * 10 + 'Wait for all environments (subprocess) to finish resetting.' + '=' * 10 | |
| ) | |
| self._logger.info( | |
| 'After sleeping {}s, the current _env_states is {}'.format( | |
| retry_waiting_time, self._env._env_states | |
| ) | |
| ) | |
| init_obs = self._env.ready_obs | |
| new_available_env_id = set(init_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) | |
| action_mask_dict[env_id] = to_ndarray(init_obs[env_id]['action_mask']) | |
| to_play_dict[env_id] = to_ndarray(init_obs[env_id]['to_play']) | |
| if self.policy_config.use_ture_chance_label_in_chance_encoder: | |
| chance_dict[env_id] = to_ndarray(init_obs[env_id]['chance']) | |
| game_segments[env_id] = GameSegment( | |
| self._env.action_space, | |
| game_segment_length=self.policy_config.game_segment_length, | |
| config=self.policy_config | |
| ) | |
| observation_window_stack[env_id] = deque( | |
| [init_obs[env_id]['observation'] for _ in range(self.policy_config.model.frame_stack_num)], | |
| maxlen=self.policy_config.model.frame_stack_num | |
| ) | |
| game_segments[env_id].reset(observation_window_stack[env_id]) | |
| last_game_segments[env_id] = None | |
| last_game_priorities[env_id] = None | |
| # log | |
| self_play_moves_max = max(self_play_moves_max, eps_steps_lst[env_id]) | |
| self_play_visit_entropy.append(visit_entropies_lst[env_id] / eps_steps_lst[env_id]) | |
| self_play_moves += eps_steps_lst[env_id] | |
| self_play_episodes += 1 | |
| pred_values_lst[env_id] = [] | |
| search_values_lst[env_id] = [] | |
| eps_steps_lst[env_id] = 0 | |
| visit_entropies_lst[env_id] = 0 | |
| # Env reset is done by env_manager automatically | |
| self._policy.reset([env_id]) | |
| self._reset_stat(env_id) | |
| # TODO(pu): subprocess mode, when n_episode > self._env_num, occasionally the ready_env_id=() | |
| # and the stack_obs is np.array(None, dtype=object) | |
| ready_env_id.remove(env_id) | |
| if collected_episode >= n_episode: | |
| # [data, meta_data] | |
| return_data = [self.game_segment_pool[i][0] for i in range(len(self.game_segment_pool))], [ | |
| { | |
| 'priorities': self.game_segment_pool[i][1], | |
| 'done': self.game_segment_pool[i][2], | |
| 'unroll_plus_td_steps': self.unroll_plus_td_steps | |
| } for i in range(len(self.game_segment_pool)) | |
| ] | |
| self.game_segment_pool.clear() | |
| # for i in range(len(self.game_segment_pool)): | |
| # print(self.game_segment_pool[i][0].obs_segment.__len__()) | |
| # print(self.game_segment_pool[i][0].reward_segment) | |
| # for i in range(len(return_data[0])): | |
| # print(return_data[0][i].reward_segment) | |
| break | |
| collected_duration = sum([d['time'] for d in self._episode_info]) | |
| # reduce data when enables DDP | |
| if self._world_size > 1: | |
| collected_step = allreduce_data(collected_step, 'sum') | |
| collected_episode = allreduce_data(collected_episode, 'sum') | |
| collected_duration = allreduce_data(collected_duration, 'sum') | |
| self._total_envstep_count += collected_step | |
| self._total_episode_count += collected_episode | |
| self._total_duration += collected_duration | |
| # 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 self._rank != 0: | |
| return | |
| 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_reward = [d['reward'] for d in self._episode_info] | |
| visit_entropy = [d['visit_entropy'] for d in self._episode_info] | |
| if self.policy_config.gumbel_algo: | |
| completed_value = [d['completed_value'] 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_reward), | |
| 'reward_std': np.std(episode_reward), | |
| 'reward_max': np.max(episode_reward), | |
| 'reward_min': np.min(episode_reward), | |
| 'total_envstep_count': self._total_envstep_count, | |
| 'total_episode_count': self._total_episode_count, | |
| 'total_duration': self._total_duration, | |
| 'visit_entropy': np.mean(visit_entropy), | |
| # 'each_reward': episode_reward, | |
| } | |
| if self.policy_config.gumbel_algo: | |
| info['completed_value'] = np.mean(completed_value) | |
| 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) | |