313 citations · 1.8k across the 83 of their papers we have counts for
7 papers · 1 filter
Game-Theoretic Multiagent Reinforcement Learning
Yaodong Yang, Chengdong Ma, Zihan Ding +4
Tremendous advances have been made in multiagent reinforcement learning (MARL). MARL corresponds to the learning problem in a multiagent system in which multiple agents learn simul…
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…
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
Hongyao Tang, Zhaopeng Meng, Jianye Hao +9
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only…
Learning to Infer User Hidden States for Online Sequential Advertising
Zhaoqing Peng, Junqi Jin, Lan Luo +11
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both im…
Multi-Agent Determinantal Q-Learning
Yaodong Yang, Ying Wen, Liheng Chen +4
Centralized training with decentralized execution has become an important paradigm in multi-agent learning. Though practical, current methods rely on restrictive assumptions to dec…
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Yaodong Yang, Jianye Hao, Guangyong Chen +5
Recently, deep multiagent reinforcement learning (MARL) has become a highly active research area as many real-world problems can be inherently viewed as multiagent systems. A parti…
Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning
Yaodong Yang, Jianye Hao, Ben Liao +4
In many real-world tasks, multiple agents must learn to coordinate with each other given their private observations and limited communication ability. Deep multiagent reinforcement…