25 citations · 56 across the 4 of their papers we have counts for
7 papers
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…