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Mingjiang Liu

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2 papers

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EAAI Journal 2026 Journal Article

Offline reinforcement learning for badminton tactical decision-making

Mingjiang Liu
Weizhi Tao
Hailong Huang

Sports data mining is becoming increasingly vital in modern competitive sports, driven by the need for athletes to continuously enhance their performance. Traditional methods of analyzing sports data rely heavily on expert experience and manual effort, which can be inefficient and unreliable. With advancements in artificial intelligence (AI), sports data is now being processed autonomously, providing more quantitative insights and more comprehensive analysis. This paper focuses on the role of tactics in sports, particularly in badminton, and explores the potential of using AI to enhance badminton tactical decision-making. We investigate the application of offline reinforcement learning (Offline RL) to develop tactical policies from pre-collected datasets, addressing challenges including algorithm design and offline policy evaluation. Specifically, we propose a new variant of conservative Q-learning (CQL), tailored for the hybrid action space to train tactical policies using the integrated offline dataset Shuttle. To evaluate these policies, we develop a preference-based reward model that aligns with tactical preferences, offering an alternative to traditional offline policy evaluation methods. Our computer-based experimental results and analysis demonstrate that the proposed method achieves higher average rewards than all baseline methods and the behavior policy used for data collection. This underscores the potential of the proposed method to enhance badminton tactical decision-making and offer athletes more effective tactical recommendations. Code and data are available at https: //github. com/Wenminggong/Offline_RL_for_Badminton.

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IROS Conference 2022 Conference Paper

Task Decoupling in Preference-based Reinforcement Learning for Personalized Human-Robot Interaction

Mingjiang Liu
Chunlin Chen

Intelligent robots designed to interact with hu-mans in the real world need to adapt to the preferences of different individuals. Preference-based reinforcement learning (RL) has shown great potential for teaching robots to learn personalized behaviors from interacting with humans with-out a meticulous, hand-crafted reward function, replaced by learning reward based on a human's preferences between two robot trajectories. However, poor feedback efficiency and poor exploration in the state and reward spaces make current preference-based RL algorithms perform poorly in complex interactive tasks. To improve the performance of preference-based RL, we incorporate prior knowledge of the task into preference-based RL. Specifically, we decouple the task from preference in human-robot interaction. We utilize a sketchy task reward derived from task priori to instruct robots to conduct more effective task exploration. Then a learned reward from preference-based RL is used to optimize the robot's policy to align with human preferences. In addition, these two parts are combined organically via reward shaping. The experimental results show that our method is a practical and effective solution for personalized human-robot interaction. Code is available at https://github.com/Wenminggong/PbRL_for_PHRI.

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