Scholarlink

<p>Prioritized Experience Replay enables the model to learn more about relatively important samples by artificially changing their accessed frequencies. However, this non-uniform sampling method shifts the state-action distribution that is originally used to estimate Q-value functions, which brings about the estimation deviation. In this article, a novel off-policy reinforcement learning training framework called Directly Attention Loss Adjusted Prioritized Experience Replay (DALAP) is proposed, which can directly quantify the changed extent of the shifted distribution through Parallel Self-Attention network, enabling precise error compensation. Furthermore, a Priority-Encouragement mechanism is designed to optimize the sample screening criteria, and enhance training efficiency. To verify the effectiveness of DALAP, a realistic environment of multi-USV, based on Unreal Engine, is constructed. Comparative experiments across multiple groups demonstrate that DALAP offers significant advantages, including faster convergence and smaller training variance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Directly Attention loss adjusted prioritized experience replay

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月25日

查看全文
  • Zhuoying Chen,
  • Huiping Li,
  • Zhaoxu Wang

摘要

Prioritized Experience Replay enables the model to learn more about relatively important samples by artificially changing their accessed frequencies. However, this non-uniform sampling method shifts the state-action distribution that is originally used to estimate Q-value functions, which brings about the estimation deviation. In this article, a novel off-policy reinforcement learning training framework called Directly Attention Loss Adjusted Prioritized Experience Replay (DALAP) is proposed, which can directly quantify the changed extent of the shifted distribution through Parallel Self-Attention network, enabling precise error compensation. Furthermore, a Priority-Encouragement mechanism is designed to optimize the sample screening criteria, and enhance training efficiency. To verify the effectiveness of DALAP, a realistic environment of multi-USV, based on Unreal Engine, is constructed. Comparative experiments across multiple groups demonstrate that DALAP offers significant advantages, including faster convergence and smaller training variance.