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import gym |
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from ditk import logging |
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from ding.model import PPG |
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from ding.policy import PPGOffPolicy |
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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.data.buffer.middleware import use_time_check, sample_range_view |
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from ding.config import compile_config |
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from ding.framework import task |
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from ding.framework.context import OnlineRLContext |
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from ding.framework.middleware import OffPolicyLearner, StepCollector, interaction_evaluator, data_pusher, \ |
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CkptSaver, gae_estimator |
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from ding.utils import set_pkg_seed |
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from dizoo.classic_control.cartpole.config.cartpole_ppg_offpolicy_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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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("CartPole-v0")) 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("CartPole-v0")) 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 = PPG(**cfg.policy.model) |
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buffer_cfg = cfg.policy.other.replay_buffer |
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max_size = max(buffer_cfg.policy.replay_buffer_size, buffer_cfg.value.replay_buffer_size) |
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buffer_ = DequeBuffer(size=max_size) |
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policy_buffer = buffer_.view() |
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policy_buffer.use(use_time_check(policy_buffer, max_use=buffer_cfg.policy.max_use)) |
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policy_buffer.use(sample_range_view(policy_buffer, start=-buffer_cfg.policy.replay_buffer_size)) |
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value_buffer = buffer_.view() |
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value_buffer.use(use_time_check(value_buffer, max_use=buffer_cfg.value.max_use)) |
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value_buffer.use(sample_range_view(value_buffer, start=-buffer_cfg.value.replay_buffer_size)) |
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policy = PPGOffPolicy(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, buffer_)) |
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task.use(OffPolicyLearner(cfg, policy.learn_mode, {'policy': policy_buffer, 'value': value_buffer})) |
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task.use(CkptSaver(policy, cfg.exp_name, train_freq=100)) |
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task.run() |
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if __name__ == "__main__": |
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main() |
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