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| from typing import Union, Optional, List, Any, Tuple | |
| import os | |
| import torch | |
| from ditk import logging | |
| import copy | |
| from functools import partial | |
| from tensorboardX import SummaryWriter | |
| from copy import deepcopy | |
| from ding.envs import get_vec_env_setting, create_env_manager | |
| from ding.worker import BaseLearner, InteractionSerialEvaluator, BaseSerialCommander, create_buffer, \ | |
| create_serial_collector | |
| from ding.config import read_config, compile_config | |
| from ding.policy import create_policy | |
| from ding.utils import set_pkg_seed | |
| from .utils import random_collect, mark_not_expert, mark_warm_up | |
| def serial_pipeline_td3_vae( | |
| input_cfg: Union[str, Tuple[dict, dict]], | |
| seed: int = 0, | |
| env_setting: Optional[List[Any]] = None, | |
| model: Optional[torch.nn.Module] = None, | |
| max_train_iter: Optional[int] = int(1e10), | |
| max_env_step: Optional[int] = int(1e10), | |
| ) -> 'Policy': # noqa | |
| """ | |
| Overview: | |
| Serial pipeline entry for VAE latent action. | |
| Arguments: | |
| - input_cfg (:obj:`Union[str, Tuple[dict, dict]]`): Config in dict type. \ | |
| ``str`` type means config file path. \ | |
| ``Tuple[dict, dict]`` type means [user_config, create_cfg]. | |
| - seed (:obj:`int`): Random seed. | |
| - env_setting (:obj:`Optional[List[Any]]`): A list with 3 elements: \ | |
| ``BaseEnv`` subclass, collector env config, and evaluator env config. | |
| - model (:obj:`Optional[torch.nn.Module]`): Instance of torch.nn.Module. | |
| - max_train_iter (:obj:`Optional[int]`): Maximum policy update iterations in training. | |
| - max_env_step (:obj:`Optional[int]`): Maximum collected environment interaction steps. | |
| Returns: | |
| - policy (:obj:`Policy`): Converged policy. | |
| """ | |
| if isinstance(input_cfg, str): | |
| cfg, create_cfg = read_config(input_cfg) | |
| else: | |
| cfg, create_cfg = deepcopy(input_cfg) | |
| create_cfg.policy.type = create_cfg.policy.type + '_command' | |
| env_fn = None if env_setting is None else env_setting[0] | |
| cfg = compile_config(cfg, seed=seed, env=env_fn, auto=True, create_cfg=create_cfg, save_cfg=True) | |
| # Create main components: env, policy | |
| if env_setting is None: | |
| env_fn, collector_env_cfg, evaluator_env_cfg = get_vec_env_setting(cfg.env) | |
| else: | |
| env_fn, collector_env_cfg, evaluator_env_cfg = env_setting | |
| collector_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in collector_env_cfg]) | |
| evaluator_env = create_env_manager(cfg.env.manager, [partial(env_fn, cfg=c) for c in evaluator_env_cfg]) | |
| collector_env.seed(cfg.seed) | |
| evaluator_env.seed(cfg.seed, dynamic_seed=False) | |
| set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda) | |
| policy = create_policy(cfg.policy, model=model, enable_field=['learn', 'collect', 'eval', 'command']) | |
| # Create worker components: learner, collector, evaluator, replay buffer, commander. | |
| tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial')) | |
| learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name) | |
| collector = create_serial_collector( | |
| cfg.policy.collect.collector, | |
| env=collector_env, | |
| policy=policy.collect_mode, | |
| tb_logger=tb_logger, | |
| exp_name=cfg.exp_name | |
| ) | |
| evaluator = InteractionSerialEvaluator( | |
| cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name | |
| ) | |
| replay_buffer = create_buffer(cfg.policy.other.replay_buffer, tb_logger=tb_logger, exp_name=cfg.exp_name) | |
| replay_buffer_recent = create_buffer(cfg.policy.other.replay_buffer, tb_logger=tb_logger, exp_name=cfg.exp_name) | |
| commander = BaseSerialCommander( | |
| cfg.policy.other.commander, learner, collector, evaluator, replay_buffer, policy.command_mode | |
| ) | |
| # ========== | |
| # Main loop | |
| # ========== | |
| # Learner's before_run hook. | |
| learner.call_hook('before_run') | |
| # Accumulate plenty of data at the beginning of training. | |
| if cfg.policy.get('random_collect_size', 0) > 0: | |
| # backup | |
| # if cfg.policy.get('transition_with_policy_data', False): | |
| # collector.reset_policy(policy.collect_mode) | |
| # else: | |
| # action_space = collector_env.action_space | |
| # random_policy = PolicyFactory.get_random_policy(policy.collect_mode, action_space=action_space) | |
| # collector.reset_policy(random_policy) | |
| # collect_kwargs = commander.step() | |
| # new_data = collector.collect(n_sample=cfg.policy.random_collect_size, policy_kwargs=collect_kwargs) | |
| # for item in new_data: | |
| # item['warm_up'] = True | |
| # replay_buffer.push(new_data, cur_collector_envstep=0) | |
| # collector.reset_policy(policy.collect_mode) | |
| # postprocess_data_fn = lambda x: mark_warm_up(mark_not_expert(x)) | |
| random_collect( | |
| cfg.policy, | |
| policy, | |
| collector, | |
| collector_env, | |
| commander, | |
| replay_buffer, | |
| postprocess_data_fn=lambda x: mark_warm_up(mark_not_expert(x)) # postprocess_data_fn | |
| ) | |
| # warm_up | |
| # Learn policy from collected data | |
| for i in range(cfg.policy.learn.warm_up_update): | |
| # Learner will train ``update_per_collect`` times in one iteration. | |
| train_data = replay_buffer.sample(learner.policy.get_attribute('batch_size'), learner.train_iter) | |
| if train_data is None: | |
| # It is possible that replay buffer's data count is too few to train ``update_per_collect`` times | |
| logging.warning( | |
| "Replay buffer's data can only train for {} steps. ".format(i) + | |
| "You can modify data collect config, e.g. increasing n_sample, n_episode." | |
| ) | |
| break | |
| learner.train(train_data, collector.envstep) | |
| if learner.policy.get_attribute('priority'): | |
| replay_buffer.update(learner.priority_info) | |
| replay_buffer.clear() # NOTE | |
| # NOTE: for the case collector_env_num>1, because after the random collect phase, self._traj_buffer[env_id] may | |
| # be not empty. Only if the condition "timestep.done or len(self._traj_buffer[env_id]) == self._traj_len" is | |
| # satisfied, the self._traj_buffer will be clear. For our alg., the data in self._traj_buffer[env_id], | |
| # latent_action=False, cannot be used in rl_vae phase. | |
| collector.reset(policy.collect_mode) | |
| count = 0 | |
| while True: | |
| collect_kwargs = commander.step() | |
| # Evaluate policy performance | |
| if evaluator.should_eval(learner.train_iter): | |
| stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep) | |
| if stop: | |
| break | |
| # Collect data by default config n_sample/n_episode | |
| new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs=collect_kwargs) | |
| for item in new_data: | |
| item['warm_up'] = False | |
| replay_buffer.push(new_data, cur_collector_envstep=collector.envstep) | |
| replay_buffer_recent.push(copy.deepcopy(new_data), cur_collector_envstep=collector.envstep) | |
| # rl phase | |
| if count % cfg.policy.learn.rl_vae_update_circle in range(0, cfg.policy.learn.rl_vae_update_circle): | |
| # Learn policy from collected data | |
| for i in range(cfg.policy.learn.update_per_collect_rl): | |
| # Learner will train ``update_per_collect`` times in one iteration. | |
| train_data = replay_buffer.sample(learner.policy.get_attribute('batch_size'), learner.train_iter) | |
| if train_data is not None: | |
| for item in train_data: | |
| item['rl_phase'] = True | |
| item['vae_phase'] = False | |
| if train_data is None: | |
| # It is possible that replay buffer's data count is too few to train ``update_per_collect`` times | |
| logging.warning( | |
| "Replay buffer's data can only train for {} steps. ".format(i) + | |
| "You can modify data collect config, e.g. increasing n_sample, n_episode." | |
| ) | |
| break | |
| learner.train(train_data, collector.envstep) | |
| if learner.policy.get_attribute('priority'): | |
| replay_buffer.update(learner.priority_info) | |
| # vae phase | |
| if count % cfg.policy.learn.rl_vae_update_circle in range(cfg.policy.learn.rl_vae_update_circle - 1, | |
| cfg.policy.learn.rl_vae_update_circle): | |
| for i in range(cfg.policy.learn.update_per_collect_vae): | |
| # Learner will train ``update_per_collect`` times in one iteration. | |
| # TODO(pu): different sample style | |
| train_data_history = replay_buffer.sample( | |
| int(learner.policy.get_attribute('batch_size') / 2), learner.train_iter | |
| ) | |
| train_data_recent = replay_buffer_recent.sample( | |
| int(learner.policy.get_attribute('batch_size') / 2), learner.train_iter | |
| ) | |
| train_data = train_data_history + train_data_recent | |
| if train_data is not None: | |
| for item in train_data: | |
| item['rl_phase'] = False | |
| item['vae_phase'] = True | |
| if train_data is None: | |
| # It is possible that replay buffer's data count is too few to train ``update_per_collect`` times | |
| logging.warning( | |
| "Replay buffer's data can only train for {} steps. ".format(i) + | |
| "You can modify data collect config, e.g. increasing n_sample, n_episode." | |
| ) | |
| break | |
| learner.train(train_data, collector.envstep) | |
| if learner.policy.get_attribute('priority'): | |
| replay_buffer.update(learner.priority_info) | |
| replay_buffer_recent.clear() # NOTE | |
| if collector.envstep >= max_env_step or learner.train_iter >= max_train_iter: | |
| break | |
| count += 1 | |
| # Learner's after_run hook. | |
| learner.call_hook('after_run') | |
| return policy | |