4 citations · 13 across the 7 of their papers we have counts for
6 papers · 1 filter
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
Ying Wen, Hui Chen, Yaodong Yang +4
Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration. Nonetheless, wh…
Learning in Nonzero-Sum Stochastic Games with Potentials
David Mguni, Yutong Wu, Yali Du +6
Multi-agent reinforcement learning (MARL) has become effective in tackling discrete cooperative game scenarios. However, MARL has yet to penetrate settings beyond those modelled by…
Bi-level Actor-Critic for Multi-agent Coordination
Haifeng Zhang, Weizhe Chen, Zeren Huang +4
Coordination is one of the essential problems in multi-agent systems. Typically multi-agent reinforcement learning (MARL) methods treat agents equally and the goal is to solve the…
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
Jiarui Jin, Ming Zhou, Weinan Zhang +9
How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-h…
Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning
Minne Li, Zhiwei, Qin +7
A fundamental question in any peer-to-peer ridesharing system is how to, both effectively and efficiently, dispatch user's ride requests to the right driver in real time. Tradition…
Mean Field Multi-Agent Reinforcement Learning
Yaodong Yang, Rui Luo, Minne Li +3
Existing multi-agent reinforcement learning methods are limited typically to a small number of agents. When the agent number increases largely, the learning becomes intractable due…