Sorrel提供简洁接口，助力研究多智能体学习与群体互动。
Sorrel: A simple and flexible framework for multi-agent reinforcement learning
- 基于直观心理结构设计智能体-环境循环
- 支持快速构建与测试多智能体环境
- 适合社会科学家探究群体动态演化
我们提出Sorrel（https://github.com/social-ai-uoft/sorrel），一个用于生成和测试多智能体强化学习环境的简单Python接口。该接口强调简洁性与可访问性，采用更符合心理学直觉的基本智能体-环境循环结构，使社会科学家能够研究学习与社会互动如何导致群体动态的发展与变化。本文简要阐述了Sorrel的设计理念与核心功能。
原文摘要 · Abstract (English)
We introduce Sorrel (https://github.com/social-ai-uoft/sorrel), a simple Python interface for generating and testing new multi-agent reinforcement learning environments. This interface places a high degree of emphasis on simplicity and accessibility, and uses a more psychologically intuitive structure for the basic agent-environment loop, making it a useful tool for social scientists to investigate how learning and social interaction leads to the development and change of group dynamics. In this short paper, we outline the basic design philosophy and features of Sorrel.
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