NegotiationGym: Self-Optimizing Agents in a Multi-Agent Social Simulation Environment
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915533885014016 |
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| author | Mangla, Shashank Hokamp, Chris Boylan, Jack Ghalandari, Demian Gholipour Jauhari, Yuuv Cassidy, Lauren Duffy, Oisin |
| author_facet | Mangla, Shashank Hokamp, Chris Boylan, Jack Ghalandari, Demian Gholipour Jauhari, Yuuv Cassidy, Lauren Duffy, Oisin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04368 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NegotiationGym: Self-Optimizing Agents in a Multi-Agent Social Simulation Environment Mangla, Shashank Hokamp, Chris Boylan, Jack Ghalandari, Demian Gholipour Jauhari, Yuuv Cassidy, Lauren Duffy, Oisin Multiagent Systems Artificial Intelligence 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. |
| title | NegotiationGym: Self-Optimizing Agents in a Multi-Agent Social Simulation Environment |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2510.04368 |