313 citations · 1.8k across the 83 of their papers we have counts for
16 papers · 1 filter
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games
Yang Li, Kun Xiong, Yingping Zhang +6
This paper presents an empirical exploration of non-transitivity in perfect-information games, specifically focusing on Xiangqi, a traditional Chinese board game comparable in game…
ProAgent: Building Proactive Cooperative Agents with Large Language Models
Ceyao Zhang, Kaijie Yang, Siyi Hu +12
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems. Current approaches to developing cooperative agents rely…
STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning
Sirui Chen, Zhaowei Zhang, Yaodong Yang +1
Centralized Training with Decentralized Execution (CTDE) has been proven to be an effective paradigm in cooperative multi-agent reinforcement learning (MARL). One of the major chal…
A Review of Safe Reinforcement Learning: Methods, Theory and Applications
Shangding Gu, Long Yang, Yali Du +4
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-making tasks. However, safety concerns are raised during deploying RL in real-world application…
Measuring the Non-Transitivity in Chess
Ricky Sanjaya, Jun Wang, Yaodong Yang
It has long been believed that Chess is the \emph{Drosophila} of Artificial Intelligence (AI). Studying Chess can productively provide valid knowledge about complex systems. Althou…
Multi-Agent Constrained Policy Optimisation
Shangding Gu, Jakub Grudzien Kuba, Munning Wen +6
Developing reinforcement learning algorithms that satisfy safety constraints is becoming increasingly important in real-world applications. In multi-agent reinforcement learning (M…
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
Jakub Grudzien Kuba, Ruiqing Chen, Muning Wen +4
Trust region methods rigorously enabled reinforcement learning (RL) agents to learn monotonically improving policies, leading to superior performance on a variety of tasks. Unfortu…
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
Tianze Zhou, Fubiao Zhang, Kun Shao +10
Extending transfer learning to cooperative multi-agent reinforcement learning (MARL) has recently received much attention. In contrast to the single-agent setting, the coordination…
Neural Auto-Curricula
Xidong Feng, Oliver Slumbers, Ziyu Wan +5
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered…
Modelling Behavioural Diversity for Learning in Open-Ended Games
Nicolas Perez Nieves, Yaodong Yang, Oliver Slumbers +3
Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e.g., Rock-Paper-Scissor…
Online Double Oracle
Le Cong Dinh, Yaodong Yang, Stephen McAleer +6
Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence. This paper proposes new learnin…
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
Yaodong Yang, Jun Luo, Ying Wen +5
Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games. A cornerstone of this success is the auto-curriculum f…
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…
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning
Ying Wen, Yaodong Yang, Rui Luo +1
Though limited in real-world decision making, most multi-agent reinforcement learning (MARL) models assume perfectly rational agents -- a property hardly met due to individual's co…
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
Yaodong Yang, Lantao Yu, Yiwei Bai +4
We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning. Our in…
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
Peng Peng, Ying Wen, Yaodong Yang +4
Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort. Efficient learning for intra-agent communication and coo…