from typing import Union, Optional, List, Any, Tuple import os import torch import logging from functools import partial from tensorboardX import SummaryWriter 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, PolicyFactory from ding.reward_model import create_reward_model from ding.utils import set_pkg_seed # from dizoo.atari.config.serial.pong.pong_trex_sql_config import main_config, create_config from dizoo.box2d.lunarlander.config.lunarlander_trex_offppo_config import main_config, create_config def serial_pipeline_reward_model_trex( input_cfg: Union[str, Tuple[dict, dict]], seed: int = 0, env_setting: Optional[List[Any]] = None, model: Optional[torch.nn.Module] = None, max_iterations: Optional[int] = int(1e10), ) -> 'Policy': # noqa """ Overview: serial_pipeline_reward_model_trex. 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_iterations (:obj:`Optional[torch.nn.Module]`): Learner's max iteration. Pipeline will stop \ when reaching this iteration. Returns: - policy (:obj:`Policy`): Converged policy. """ if isinstance(input_cfg, str): cfg, create_cfg = read_config(input_cfg) else: cfg, create_cfg = input_cfg create_cfg.policy.type = create_cfg.policy.type + '_command' create_cfg.reward_model = dict(type='trex') 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, policy.collect_mode.get_attribute('device'), tb_logger) reward_model.train() # ========== # 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: if cfg.policy.get('transition_with_policy_data', False): collector.reset_policy(policy.collect_mode) else: action_space = collector_env.env_info().act_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) replay_buffer.push(new_data, cur_collector_envstep=0) collector.reset_policy(policy.collect_mode) for _ in range(max_iterations): 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 if hasattr(cfg.policy.collect, "each_iter_n_sample"): # TODO(pu) new_data = collector.collect( n_sample=cfg.policy.collect.each_iter_n_sample, train_iter=learner.train_iter, policy_kwargs=collect_kwargs ) else: new_data = collector.collect(train_iter=learner.train_iter, policy_kwargs=collect_kwargs) replay_buffer.push(new_data, cur_collector_envstep=collector.envstep) # 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 reward_model.estimate(train_data) learner.train(train_data, collector.envstep) if learner.policy.get_attribute('priority'): replay_buffer.update(learner.priority_info) # Learner's after_run hook. learner.call_hook('after_run') return policy if __name__ == '__main__': serial_pipeline_reward_model_trex([main_config, create_config])