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| from copy import deepcopy | |
| from ditk import logging | |
| from ding.model import DQN | |
| from ding.policy import DQNPolicy | |
| 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 OffPolicyLearner, StepCollector, interaction_evaluator, data_pusher, \ | |
| eps_greedy_handler, CkptSaver, context_exchanger, model_exchanger, termination_checker, nstep_reward_enhancer, \ | |
| online_logger | |
| from ding.utils import set_pkg_seed | |
| from dizoo.atari.envs.atari_env import AtariEnv | |
| from dizoo.atari.config.serial.pong.pong_dqn_config import main_config, create_config | |
| def main(): | |
| logging.getLogger().setLevel(logging.INFO) | |
| main_config.exp_name = 'pong_dqn_seed0_ditask_dist' | |
| cfg = compile_config(main_config, create_cfg=create_config, auto=True) | |
| ding_init(cfg) | |
| with task.start(async_mode=False, ctx=OnlineRLContext()): | |
| assert task.router.is_active, "Please execute this script with ditask! See note in the header." | |
| set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda) | |
| model = DQN(**cfg.policy.model) | |
| policy = DQNPolicy(cfg.policy, model=model) | |
| if 'learner' in task.router.labels: | |
| logging.info("Learner running on node {}".format(task.router.node_id)) | |
| buffer_ = DequeBuffer(size=cfg.policy.other.replay_buffer.replay_buffer_size) | |
| task.use( | |
| context_exchanger( | |
| send_keys=["train_iter"], | |
| recv_keys=["trajectories", "episodes", "env_step", "env_episode"], | |
| skip_n_iter=0 | |
| ) | |
| ) | |
| task.use(model_exchanger(model, is_learner=True)) | |
| task.use(nstep_reward_enhancer(cfg)) | |
| task.use(data_pusher(cfg, buffer_)) | |
| task.use(OffPolicyLearner(cfg, policy.learn_mode, buffer_)) | |
| task.use(CkptSaver(policy, cfg.exp_name, train_freq=1000)) | |
| elif 'evaluator' in task.router.labels: | |
| logging.info("Evaluator running on node {}".format(task.router.node_id)) | |
| evaluator_cfg = deepcopy(cfg.env) | |
| evaluator_cfg.is_train = False | |
| evaluator_env = SubprocessEnvManagerV2( | |
| env_fn=[lambda: AtariEnv(evaluator_cfg) for _ in range(cfg.env.evaluator_env_num)], cfg=cfg.env.manager | |
| ) | |
| task.use(context_exchanger(recv_keys=["train_iter", "env_step"], skip_n_iter=1)) | |
| task.use(model_exchanger(model, is_learner=False)) | |
| task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env)) | |
| task.use(CkptSaver(policy, cfg.exp_name, save_finish=False)) | |
| task.use(online_logger(record_train_iter=True)) | |
| elif 'collector' in task.router.labels: | |
| logging.info("Collector running on node {}".format(task.router.node_id)) | |
| collector_cfg = deepcopy(cfg.env) | |
| collector_cfg.is_train = True | |
| collector_env = SubprocessEnvManagerV2( | |
| env_fn=[lambda: AtariEnv(collector_cfg) for _ in range(cfg.env.collector_env_num)], cfg=cfg.env.manager | |
| ) | |
| task.use( | |
| context_exchanger( | |
| send_keys=["trajectories", "episodes", "env_step", "env_episode"], | |
| recv_keys=["train_iter"], | |
| skip_n_iter=1 | |
| ) | |
| ) | |
| task.use(model_exchanger(model, is_learner=False)) | |
| task.use(eps_greedy_handler(cfg)) | |
| task.use(StepCollector(cfg, policy.collect_mode, collector_env)) | |
| task.use(termination_checker(max_env_step=int(1e7))) | |
| else: | |
| raise KeyError("invalid router labels: {}".format(task.router.labels)) | |
| task.run() | |
| if __name__ == "__main__": | |
| main() | |