让智能体在模拟社交中自我优化谈判策略
NegotiationGym: Self-Optimizing Agents in a Multi-Agent Social Simulation Environment
- 通过多轮交互自适应调整谈判策略
- 支持灵活配置的多人协作仿真环境
- 适合研究博弈行为与智能体演化
我们设计并实现了NegotiationGym，一个面向谈判与合作的多智能体社会仿真环境的API和用户界面。该代码库提供直观、基于配置的API，便于快速构建和定制仿真场景。每个智能体通过其代理级效用函数定义优化目标，可通过与其它智能体进行多轮交互，观察结果并调整自身策略以实现自我优化。
原文摘要 · Abstract (English)
We design and implement NegotiationGym, an API and user interface for configuring and running multi-agent social simulations focused upon negotiation and cooperation. The NegotiationGym codebase offers a user-friendly, configuration-driven API that enables easy design and customization of simulation scenarios. Agent-level utility functions encode optimization criteria for each agent, and agents can self-optimize by conducting multiple interaction rounds with other agents, observing outcomes, and modifying their strategies for future rounds.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。