NegotiationGym: Self-Optimizing Agents in a Multi-Agent Social Simulation Environment

Shashank Mangla, Chris Hokamp, Jack Boylan, Demian Gholipour Ghalandari, Yuuv Jauhari, Lauren Cassidy, Oisin Duffy

Keywords: simulation, multi-agent, utility

TL;DR: A demo environment for running multi-agent simulations where agents can observe outcomes and self-optimize.

Abstract: 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.

Submission Number: 21