| from copy import deepcopy | |
| from ditk import logging | |
| from ding.model import VAC | |
| from ding.policy import PPOPolicy | |
| from ding.envs import DingEnvWrapper, SubprocessEnvManagerV2 | |
| from ding.data import DequeBuffer | |
| from ding.config import compile_config | |
| from ding.framework import task, ding_init | |
| from ding.framework.context import OnlineRLContext | |
| from ding.framework.middleware import multistep_trainer, StepCollector, interaction_evaluator, CkptSaver, \ | |
| gae_estimator, ddp_termination_checker, online_logger | |
| from ding.utils import set_pkg_seed, DistContext, get_rank, get_world_size | |
| from dizoo.atari.envs.atari_env import AtariEnv | |
| from dizoo.atari.config.serial.pong.pong_onppo_config import main_config, create_config | |
| def main(): | |
| logging.getLogger().setLevel(logging.INFO) | |
| with DistContext(): | |
| rank, world_size = get_rank(), get_world_size() | |
| main_config.example = 'pong_ppo_seed0_ddp_avgsplit' | |
| main_config.policy.multi_gpu = True | |
| main_config.policy.learn.batch_size = main_config.policy.learn.batch_size // world_size | |
| main_config.policy.collect.n_sample = main_config.policy.collect.n_sample // world_size | |
| cfg = compile_config(main_config, create_cfg=create_config, auto=True) | |
| ding_init(cfg) | |
| with task.start(async_mode=False, ctx=OnlineRLContext()): | |
| collector_cfg = deepcopy(cfg.env) | |
| collector_cfg.is_train = True | |
| evaluator_cfg = deepcopy(cfg.env) | |
| evaluator_cfg.is_train = False | |
| collector_env = SubprocessEnvManagerV2( | |
| env_fn=[lambda: AtariEnv(collector_cfg) for _ in range(cfg.env.collector_env_num)], cfg=cfg.env.manager | |
| ) | |
| evaluator_env = SubprocessEnvManagerV2( | |
| env_fn=[lambda: AtariEnv(evaluator_cfg) for _ in range(cfg.env.evaluator_env_num)], cfg=cfg.env.manager | |
| ) | |
| set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda) | |
| model = VAC(**cfg.policy.model) | |
| policy = PPOPolicy(cfg.policy, model=model) | |
| if rank == 0: | |
| task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env)) | |
| task.use(StepCollector(cfg, policy.collect_mode, collector_env)) | |
| task.use(gae_estimator(cfg, policy.collect_mode)) | |
| task.use(multistep_trainer(cfg, policy.learn_mode)) | |
| if rank == 0: | |
| task.use(CkptSaver(policy, cfg.exp_name, train_freq=1000)) | |
| task.use(ddp_termination_checker(max_env_step=int(1e7), rank=rank)) | |
| task.run() | |
| if __name__ == "__main__": | |
| main() | |