DI-engine/ding/entry/serial_entry_td3_vae.py

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