Reflection of Episodes: Learning to Play Game from Expert and Self Experiences
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914463643336704 |
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| author | Xu, Xiaojie Li, Zongyuan Lu, Chang Qi, Runnan Ni, Yanan Jiang, Lumin Liu, Xiangbei Zhang, Xuebo Fang, Yongchun Huang, Kuihua Guo, Xian Wu, Zhanghua Li, Zhenya |
| author_facet | Xu, Xiaojie Li, Zongyuan Lu, Chang Qi, Runnan Ni, Yanan Jiang, Lumin Liu, Xiangbei Zhang, Xuebo Fang, Yongchun Huang, Kuihua Guo, Xian Wu, Zhanghua Li, Zhenya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reflection of Episodes: Learning to Play Game from Expert and Self Experiences Xu, Xiaojie Li, Zongyuan Lu, Chang Qi, Runnan Ni, Yanan Jiang, Lumin Liu, Xiangbei Zhang, Xuebo Fang, Yongchun Huang, Kuihua Guo, Xian Wu, Zhanghua Li, Zhenya Artificial Intelligence 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. |
| title | Reflection of Episodes: Learning to Play Game from Expert and Self Experiences |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.13388 |