113 citations · 265 across the 10 of their papers we have counts for
10 papers
Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning
Jiachen Yang, Ethan Wang, Rakshit Trivedi +2
Critical sectors of human society are progressing toward the adoption of powerful artificial intelligence (AI) agents, which are trained individually on behalf of self-interested p…
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
Yan Li, Lingxiao Wang, Jiachen Yang +4
Multi-agent reinforcement learning (MARL) becomes more challenging in the presence of more agents, as the capacity of the joint state and action spaces grows exponentially in the n…
GraphOpt: Learning Optimization Models of Graph Formation
Rakshit Trivedi, Jiachen Yang, Hongyuan Zha
Formation mechanisms are fundamental to the study of complex networks, but learning them from observations is challenging. In real-world domains, one often has access only to the f…
Learning to Incentivize Other Learning Agents
Jiachen Yang, Ang Li, Mehrdad Farajtabar +3
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years. Much of this effort has focused on the single…
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery
Jiachen Yang, Igor Borovikov, Hongyuan Zha
Human players in professional team sports achieve high level coordination by dynamically choosing complementary skills and executing primitive actions to perform these skills. As a…
Single Episode Policy Transfer in Reinforcement Learning
Jiachen Yang, Brenden Petersen, Hongyuan Zha +1
Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL). An even greater challenge is performing near-optimally in a single…
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization
Zhi Zhang, Jiachen Yang, Hongyuan Zha
Traffic congestion in metropolitan areas is a world-wide problem that can be ameliorated by traffic lights that respond dynamically to real-time conditions. Recent studies applying…
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
Jiachen Yang, Alireza Nakhaei, David Isele +2
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses…
Learning Deep Mean Field Games for Modeling Large Population Behavior
Jiachen Yang, Xiaojing Ye, Rakshit Trivedi +2
We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete t…
Fake News Mitigation via Point Process Based Intervention
Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye +6
We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. Th…