import gym from ditk import logging from ding.model import MAQAC from ding.policy import SACDiscretePolicy from ding.envs import DingEnvWrapper, BaseEnvManagerV2 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, CkptSaver, \ data_pusher, online_logger, termination_checker, eps_greedy_handler from ding.utils import set_pkg_seed from dizoo.petting_zoo.config.ptz_simple_spread_masac_config import main_config, create_config from dizoo.petting_zoo.envs.petting_zoo_simple_spread_env import PettingZooEnv def main(): logging.getLogger().setLevel(logging.INFO) cfg = compile_config(main_config, create_cfg=create_config, auto=True) ding_init(cfg) with task.start(async_mode=False, ctx=OnlineRLContext()): collector_env = BaseEnvManagerV2( env_fn=[lambda: PettingZooEnv(cfg.env) for _ in range(cfg.env.collector_env_num)], cfg=cfg.env.manager ) evaluator_env = BaseEnvManagerV2( env_fn=[lambda: PettingZooEnv(cfg.env) for _ in range(cfg.env.evaluator_env_num)], cfg=cfg.env.manager ) set_pkg_seed(cfg.seed, use_cuda=cfg.policy.cuda) model = MAQAC(**cfg.policy.model) buffer_ = DequeBuffer(size=cfg.policy.other.replay_buffer.replay_buffer_size) policy = SACDiscretePolicy(cfg.policy, model=model) task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env)) task.use(eps_greedy_handler(cfg)) task.use( StepCollector(cfg, policy.collect_mode, collector_env, random_collect_size=cfg.policy.random_collect_size) ) task.use(data_pusher(cfg, buffer_)) task.use(OffPolicyLearner(cfg, policy.learn_mode, buffer_, log_freq=100)) task.use(CkptSaver(policy, cfg.exp_name, train_freq=1000)) task.use(online_logger(train_show_freq=10)) task.use(termination_checker(max_env_step=int(1e6))) task.run() if __name__ == "__main__": main()