Agents
Credit Assignment For Collective Multiagent RL With Global Rewards
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Neural Information Processing Systems http://nips.cc/
Distinguishing Distributions When Samples Are Strategically Transformed
Hanrui Zhang, Yu Cheng, Vincent Conitzer
Neural Information Processing Systems http://nips.cc/
A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents
YAN ZHENG, Zhaopeng Meng, Jianye Hao, Zongzhang Zhang, Tianpei Yang, Changjie Fan
Inmultiagent domains, coping withnon-stationary agents thatchange behaviors from time to time is a challenging problem, where an agent is usually required to be able to quickly detect the other agent's policy during online interaction, and then adapt its own policy accordingly.
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.
Combinatorial Group Testing with Selfish Agents
We study the Combinatorial Group Testing (CGT) problems in a novel game-theoretic framework, with a solution concept of Adversarial Equilibrium (AE).
