568 citations · 6.2k across the 157 of their papers we have counts for
12 papers · 1 filter
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
Muning Wen, Jakub Grudzien Kuba, Runji Lin +4
Large sequence model (SM) such as GPT series and BERT has displayed outstanding performance and generalization capabilities on vision, language, and recently reinforcement learning…
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Ming Zhou, Ziyu Wan, Hanjing Wang +6
Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms, which produces a self-generated se…
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
Ming Zhou, Jun Luo, Julian Villella +34
Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently i…
Multi-Agent Interactions Modeling with Correlated Policies
Minghuan Liu, Ming Zhou, Weinan Zhang +4
In multi-agent systems, complex interacting behaviors arise due to the high correlations among agents. However, previous work on modeling multi-agent interactions from demonstratio…
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching
Ming Zhou, Jiarui Jin, Weinan Zhang +6
Improving the efficiency of dispatching orders to vehicles is a research hotspot in online ride-hailing systems. Most of the existing solutions for order-dispatching are centralize…
Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning
Liheng Chen, Hongyi Guo, Yali Du +7
In many real-world problems, a team of agents need to collaborate to maximize the common reward. Although existing works formulate this problem into a centralized learning with dec…
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…
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
Huichu Zhang, Siyuan Feng, Chang Liu +7
Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic…
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…
CoLight: Learning Network-level Cooperation for Traffic Signal Control
Hua Wei, Nan Xu, Huichu Zhang +7
Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating t…
Factorized Q-Learning for Large-Scale Multi-Agent Systems
Ming Zhou, Yong Chen, Ying Wen +5
Deep Q-learning has achieved significant success in single-agent decision making tasks. However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to…
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…