ChessGPT：连接策略学习和语言建模

摘要

在解决决策任务时，人类通常依赖于两个关键信息源：（1）历史政策数据，提供来自环境的交互重现，以及（2）自然语言形式的分析洞察，揭示宝贵的思考过程或战略考虑。尽管如此，先前的大部分研究侧重于仅使用一个信息源：它们要么仅使用历史重现来直接学习政策或值函数，要么从事语言模型训练，利用纯粹的语言语料库。在本文中，我们认为一个强大的自主代理应该涵盖这两个信息源。因此，我们提出了ChessGPT，这是一个将政策学习和语言建模相结合的GPT模型，通过整合来自这两个信息源的数据在国际象棋游戏中。具体来说，我们构建了一个与国际象棋相关的大规模游戏和语言数据集。利用这个数据集，我们展示了两个模型示例ChessCLIP和ChessGPT，集成了政策学习和语言建模。最后，我们提出了一个完整的评估框架，用于评估语言模型在国际象棋方面的能力。实验结果验证了我们模型和数据集的有效性。我们在https://github.com/waterhorse1/ChessGPT上开源我们的代码、模型和数据集。

English

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.

ChessGPT：连接策略学习和语言建模

ChessGPT: Bridging Policy Learning and Language Modeling

摘要

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