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<p>Hierarchical Reinforcement Learning (HRL) is widely applied in various complex task scenarios. In complex tasks where simple model-free reinforcement learning struggles, hierarchical design allows for more efficient utilization of interactive data, significantly reducing training costs and improving training success rates. This study delves into the use of HRL based on the model-free policy layer to learn complex strategies for a robotic arm playing table tennis. Through processes such as pre-training, self-play training, and self-play training with top-level winning strategies, the robustness of the lower-level hitting strategies has been enhanced. Furthermore, a novel decay reward mechanism has been employed in the training of the higher-level agent to improve the win rate in adversarial matches against other methods. After pre-training and adversarial training, we achieved an average of 52 rally cycles for the forehand strategy and 48 rally cycles for the backhand strategy in testing. The high-level strategy training based on the decay reward mechanism resulted in an advantageous score when competing against other strategies.</p>

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Mastering table tennis with hierarchy: a reinforcement learning approach with progressive self-play training

  • 期刊论文

  • 发表日期：

    2025年3月24日

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  • Hongxu Ma,
  • Jianyin Fan,
  • Haoran Xu,
  • Qiang Wang

摘要

Hierarchical Reinforcement Learning (HRL) is widely applied in various complex task scenarios. In complex tasks where simple model-free reinforcement learning struggles, hierarchical design allows for more efficient utilization of interactive data, significantly reducing training costs and improving training success rates. This study delves into the use of HRL based on the model-free policy layer to learn complex strategies for a robotic arm playing table tennis. Through processes such as pre-training, self-play training, and self-play training with top-level winning strategies, the robustness of the lower-level hitting strategies has been enhanced. Furthermore, a novel decay reward mechanism has been employed in the training of the higher-level agent to improve the win rate in adversarial matches against other methods. After pre-training and adversarial training, we achieved an average of 52 rally cycles for the forehand strategy and 48 rally cycles for the backhand strategy in testing. The high-level strategy training based on the decay reward mechanism resulted in an advantageous score when competing against other strategies.