title: ChessGPT: Bridging Policy Learning and Language Modeling
summary: When solving decision-making tasks, humans typically depend on information
  from two key sources: (1) Historical policy data, which provides interaction
  replay from the environment, and (2) Analytical insights in natural language
  form, exposing the invaluable thought process or strategic considerations.
  Despite this, the majority of preceding research focuses on only one source:
  they either use historical replay exclusively to directly learn policy or value
  functions, or engaged in language model training utilizing mere language
  corpus. In this paper, we argue that a powerful autonomous agent should cover
  both sources. Thus, we propose ChessGPT, a GPT model bridging policy learning
  and language modeling by integrating data from these two sources in Chess
  games. Specifically, we build a large-scale game and language dataset related
  to chess. Leveraging the dataset, we showcase two model examples ChessCLIP and
  ChessGPT, integrating policy learning and language modeling. Finally, we
  propose a full evaluation framework for evaluating language model's chess
  ability. Experimental results validate our model and dataset's effectiveness.
  We open source our code, model, and dataset at
  https://github.com/waterhorse1/ChessGPT.
paper_authors._id: 648be5a02d180f88bd6f24f4
paper_authors.name: Xidong Feng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-16T17:21:58.913Z
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paper_authors.avatarUrl: /avatars/3309f1c8f1340a93e8b7a22fb38069fa.svg
paper_authors.fullname: Xidong Feng
paper_authors.type: user
paper_authors.user: Waterhorse
paper_authors._id: 648be5a02d180f88bd6f24f5
paper_authors.name: Yicheng Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-16T17:22:36.525Z
paper_authors._id: 6422b65af1370e1444fad44a
paper_authors.avatarUrl: /avatars/8936667d7c6200ad3058f22329df5542.svg
paper_authors.fullname: Yicheng Luo
paper_authors.type: user
paper_authors.user: ethanluoyc
paper_authors._id: 648be5a02d180f88bd6f24f6
paper_authors.name: Ziyan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-10T09:25:15.878Z
paper_authors._id: 645fa37b10b42b21cb122326
paper_authors.avatarUrl: /avatars/f5ef14e60f06dd99e4c6ff4df0f225c4.svg
paper_authors.fullname: ziyan wang
paper_authors.type: user
paper_authors.user: ziyan98
paper_authors._id: 648be5a02d180f88bd6f24f7
paper_authors.name: Hongrui Tang
paper_authors._id: 648be5a02d180f88bd6f24f8
paper_authors.name: Mengyue Yang
paper_authors._id: 648be5a02d180f88bd6f24f9
paper_authors.name: Kun Shao
paper_authors._id: 648be5a02d180f88bd6f24fa
paper_authors.name: David Mguni
paper_authors._id: 648be5a02d180f88bd6f24fb
paper_authors.name: Yali Du
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-16T17:25:58.126Z
paper_authors._id: 646b3c09628e5b50b2dd1174
paper_authors.avatarUrl: /avatars/f96a4d375ea44d10891f4b47b7d7986e.svg
paper_authors.fullname: yali du
paper_authors.type: user
paper_authors.user: YaliDU
paper_authors._id: 648be5a02d180f88bd6f24fc
paper_authors.name: Jun Wang
paper_title: ChessGPT: Bridging Policy Learning and Language Modeling
paper_summary: When solving decision-making tasks, humans typically depend on information
  from two key sources: (1) Historical policy data, which provides interaction
  replay from the environment, and (2) Analytical insights in natural language
  form, exposing the invaluable thought process or strategic considerations.
  Despite this, the majority of preceding research focuses on only one source:
  they either use historical replay exclusively to directly learn policy or value
  functions, or engaged in language model training utilizing mere language
  corpus. In this paper, we argue that a powerful autonomous agent should cover
  both sources. Thus, we propose ChessGPT, a GPT model bridging policy learning
  and language modeling by integrating data from these two sources in Chess
  games. Specifically, we build a large-scale game and language dataset related
  to chess. Leveraging the dataset, we showcase two model examples ChessCLIP and
  ChessGPT, integrating policy learning and language modeling. Finally, we
  propose a full evaluation framework for evaluating language model's chess
  ability. Experimental results validate our model and dataset's effectiveness.
  We open source our code, model, and dataset at
  https://github.com/waterhorse1/ChessGPT.
paper_ai_summary: ChessGPT integrates policy learning and language modeling by combining historical chess data and analytical insights to improve autonomous decision-making in chess games.
paper_ai_keywords: GPT model ChessCLIP ChessGPT policy learning language modeling game dataset language dataset evaluation framework chess ability