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| from typing import Union, Optional, List, Any, Tuple | |
| import os | |
| import numpy as np | |
| import torch | |
| from ditk import logging | |
| 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.reward_model import create_reward_model | |
| from ding.utils import set_pkg_seed | |
| from .utils import random_collect | |
| def serial_pipeline_reward_model_offpolicy( | |
| 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 off-policy RL with reward model. | |
| 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) | |
| commander = BaseSerialCommander( | |
| cfg.policy.other.commander, learner, collector, evaluator, replay_buffer, policy.command_mode | |
| ) | |
| reward_model = create_reward_model(cfg.reward_model, policy.collect_mode.get_attribute('device'), tb_logger) | |
| # ========== | |
| # 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: | |
| random_collect(cfg.policy, policy, collector, collector_env, commander, replay_buffer) | |
| count = 0 | |
| best_return = -np.inf | |
| while True: | |
| collect_kwargs = commander.step() | |
| # Evaluate policy performance | |
| if evaluator.should_eval(learner.train_iter): | |
| stop, eval_info = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep) | |
| eval_return_mean = np.mean(eval_info['eval_episode_return']) | |
| if eval_return_mean >= best_return: | |
| reward_model.save(path=cfg.exp_name, name='best') | |
| best_return = eval_return_mean | |
| if stop: | |
| break | |
| new_data_count, target_new_data_count = 0, cfg.reward_model.get('target_new_data_count', 1) | |
| while new_data_count < target_new_data_count: | |
| new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs=collect_kwargs) | |
| new_data_count += len(new_data) | |
| # collect data for reward_model training | |
| reward_model.collect_data(new_data) | |
| replay_buffer.push(new_data, cur_collector_envstep=collector.envstep) | |
| # update reward_model | |
| reward_model.train() | |
| # clear buffer per fix iters to make sure replay buffer's data count isn't too few. | |
| if count % cfg.reward_model.clear_buffer_per_iters == 0: | |
| reward_model.clear_data() | |
| # Learn policy from collected data | |
| for i in range(cfg.policy.learn.update_per_collect): | |
| # 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 | |
| # update train_data reward using the augmented reward | |
| train_data_augmented = reward_model.estimate(train_data) | |
| learner.train(train_data_augmented, collector.envstep) | |
| if learner.policy.get_attribute('priority'): | |
| replay_buffer.update(learner.priority_info) | |
| 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') | |
| reward_model.save(path=cfg.exp_name, name='last') | |
| return policy | |