Agent Societies
Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows
In many machine learning tasks, there are commonly sources of exogenous and endogenous distribution shift, necessitating that the algorithm be retrained repeatedly over time.
Diversity Is Not All You Need: Training A Robust Cooperative Agent Needs Specialist Partners
In this paper, we show that partner specialization, in addition to diversity, is crucial for the robustness of a downstream generalist agent.
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
In fact, the interaction of these two aspects requires addressing the fact that each agent's own safety constraint requires information from all others.
Credit Assignment For Collective Multiagent RL With Global Rewards
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Neural Information Processing Systems http://nips.cc/
ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination Xihuai Wang
The significant difference between the deployment-time partners' distribution and the training partners' distribution determined by the
Inequity aversion improves cooperation in intertemporal social dilemmas
Edward Hughes, Joel Z. Leibo, Matthew Phillips, Karl Tuyls, Edgar Dueรฑez-Guzman, Antonio Garcรญa Castaรฑeda, Iain Dunning, Tina Zhu, Kevin McKee, Raphael Koster, Heather Roff, Thore Graepel
Neural Information Processing Systems http://nips.cc/
GroundedAnswersforMulti-agentDecision-making ProblemthroughGenerativeWorldModel
Theempirical results demonstrate that this framework can improve the answers for multi-agent decision-making problems by showing superior performance on the training and unseen tasks of the StarCraft Multi-Agent Challenge benchmark.
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
Chao Qu, Shie Mannor, Huan Xu, Yuan Qi, Le Song, Junwu Xiong
Neural Information Processing Systems http://nips.cc/