  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.
