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import gym |
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from ditk import logging |
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from ding.model import VAC |
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from ding.policy import PPOPolicy |
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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 multistep_trainer, StepCollector, interaction_evaluator, CkptSaver, \ |
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gae_estimator, online_logger |
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from ding.utils import set_pkg_seed |
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from dizoo.box2d.lunarlander.config.lunarlander_ppo_config import main_config, create_config |
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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: DingEnvWrapper(gym.make("LunarLander-v2")) for _ in range(cfg.env.collector_env_num)], |
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cfg=cfg.env.manager |
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) |
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evaluator_env = BaseEnvManagerV2( |
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env_fn=[lambda: DingEnvWrapper(gym.make("LunarLander-v2")) for _ in range(cfg.env.evaluator_env_num)], |
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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 = VAC(**cfg.policy.model) |
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policy = PPOPolicy(cfg.policy, model=model) |
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task.use(interaction_evaluator(cfg, policy.eval_mode, evaluator_env)) |
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task.use(StepCollector(cfg, policy.collect_mode, collector_env)) |
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task.use(gae_estimator(cfg, policy.collect_mode)) |
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task.use(multistep_trainer(policy.learn_mode, log_freq=50)) |
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task.use(CkptSaver(policy, cfg.exp_name, train_freq=100)) |
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task.use(online_logger(train_show_freq=3)) |
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task.run() |
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if __name__ == "__main__": |
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main() |
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