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