The Go Transformer: Natural Language Modeling for Game Play

The Chess Transformer: Mastering Play using Generative Language Models

This work demonstrates that natural language transformers can support more generic strategic modeling, particularly for text-archived games. In addition to learning natural language skills, the abstract transformer architecture can generate meaningful moves on a chessboard. With further fine-tuning, the transformer learns complex gameplay by training on 2.8 million chess games in Portable Game Notation. After 30,000 training steps, OpenAI's Generative Pre-trained Transformer (GPT-2) optimizes weights for 774 million parameters. This fine-tuned Chess Transformer generates plausible strategies a…

自监督行为克隆 Transformer 是文本游戏的路径爬虫

Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games

针对文本游戏中行为克隆依赖监督数据的问题，提出自监督探索奖励轨迹并用小模型筛选通用数据的 Transformer 方法，在三个基准上达到监督系统约 90% 的性能。

Learning Chess With Language Models and Transformers

Representing a board game and its positions by text-based notation enables the possibility of NLP applications. Language models, can help gain insight into a variety of interesting problems such as unsupervised learning rules of a game, detecting player behavior patterns, player attribution, and ultimately learning the game to beat state of the art. In this study, we applied BERT models, first to the simple Nim game to analyze its performance in the presence of noise in a setup of a few-shot learning architecture. We analyzed the model performance via three virtual players, namely Nim Guru, Ra…

面向定义质量估计的基于Transformer的逆向词典模型

Towards a Transformer-Based Reverse Dictionary Model for Quality Estimation of Definitions

本文比较了多种基于Transformer的模型在逆向词典任务中的表现，并探索其在严肃游戏The Dictionary Game中的应用。

涌现的世界信念：探索变换器在随机游戏中的应用

Emergent World Beliefs: Exploring Transformers in Stochastic Games

本文研究了LLMs在扑克等信息不完全领域中学习随机环境表示的能力，通过预训练和内部激活探测，展示了模型能自主学习确定性和随机性特征。

Behavior Cloned Transformers are Neurosymbolic Reasoners

In this work, we explore techniques for augmenting interactive agents with information from symbolic modules, much like humans use tools like calculators and GPS systems to assist with arithmetic and navigation. We test our agent's abilities in text games -- challenging benchmarks for evaluating the multi-step reasoning abilities of game agents in grounded, language-based environments. Our experimental study indicates that injecting the actions from these symbolic modules into the action space of a behavior cloned transformer agent increases performance on four text game benchmarks that test a…