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