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Reflection of Episodes: Learning to Play Game from Expert and Self Experiences

19 February 2025
Xiaojie Xu
Zongyuan Li
Chang Lu
Runnan Qi
Yanan Ni
Lumin Jiang
Xiangbei Liu
X. Zhang
Yongchun Fang
Kuihua Huang
Xian Guo
Zhanghua Wu
Zhenya Li
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Abstract

StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Large Language Model(LLM) learning in complex environments through self-reflection, we propose a Reflection of Episodes(ROE) framework based on expert experience and self-experience. This framework first obtains key information in the game through a keyframe selection method, then makes decisions based on expert experience and self-experience. After a game is completed, it reflects on the previous experience to obtain new self-experience. Finally, in the experiment, our method beat the robot under the Very Hard difficulty in TextStarCraft II. We analyze the data of the LLM in the process of the game in detail, verified its effectiveness.

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@article{xu2025_2502.13388,
  title={ Reflection of Episodes: Learning to Play Game from Expert and Self Experiences },
  author={ Xiaojie Xu and Zongyuan Li and Chang Lu and Runnan Qi and Yanan Ni and Lumin Jiang and Xiangbei Liu and Xuebo Zhang and Yongchun Fang and Kuihua Huang and Xian Guo and Zhanghua Wu and Zhenya Li },
  journal={arXiv preprint arXiv:2502.13388},
  year={ 2025 }
}
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