Real-world cooperation often requires intensive coordination among agents simultaneously. This task has been extensively studied within the framework of cooperative multi-agent reinforcement learning (MARL), and value decomposition methods are among those cutting-edge solutions. However, traditional methods that learn the value function as a monotonic mixing of per-agent utilities cannot solve the tasks with non-monotonic returns. This hinders their application in generic scenarios. Recent methods tackle this problem from the perspective of implicit credit assignment by learning value functions with complete expressiveness or using additional structures to improve cooperation. However, they are either difficult to learn due to large joint action spaces or insufficient to capture the complicated interactions among agents which are essential to solving tasks with non-monotonic returns. To address these problems, we propose a novel explicit credit assignment method to address the non-monotonic problem. Our method, Adaptive Value decomposition with Greedy Marginal contribution (AVGM), is based on an adaptive value decomposition that learns the cooperative value of a group of dynamically changing agents. We first illustrate that the proposed value decomposition can consider the complicated interactions among agents and is feasible to learn in large-scale scenarios. Then, our method uses a greedy marginal contribution computed from the value decomposition as an individual credit to incentivize agents to learn the optimal cooperative policy. We further extend the module with an action encoder to guarantee the linear time complexity for computing the greedy marginal contribution. Experimental results demonstrate that our method achieves significant performance improvements in several non-monotonic domains.
Efficient Querying for Cooperative Probabilistic Commitments
Artificial Intelligence Institute, University of South Carolina（南卡罗来纳大学人工智能研究所）；Computer Science and Engineering, University of Michigan（密歇根大学计算机科学与工程学院）
Cooperative Game-Theoretic Credit Assignment for Multi-Agent Policy Gradients via the Core
Key Laboratory of Intelligence, Games and Information Processing in Shaanxi Province（陕西省智能信息处理重点实验室）；Unmanned System Research Institute（无人系统研究院）；Northwestern Polytechnical University（西北工业大学）；School of Mathematics and Statistics（数学与统计学院）；Information Science Academy（信息科学学院）；China Electronics Technology Group Corporation（中国电子科技集团有限公司）
Exit-and-Join Dynamics and Equilibrium in Continuum Cooperative Games
Department of Electrical and Computer Engineering（电气与计算机工程系）；New York University Tandon School of Engineering（纽约大学塔恩工程学院）；Brooklyn, NY, USA（布鲁克林，纽约，美国）
Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient
School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China（计算机科学与技术学院，北京交通大学，北京100044，中国）；Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence（交通数据挖掘与具身智能北京市重点实验室）
Pruning as a Cooperative Game: Surrogate-Assisted Layer Contribution Estimation for Large Language Models
Shenzhen Future Network of Intelligence Institute（深圳未来网络智能研究院）；Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来网络智能重点实验室）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Beijing Normal University（北京师范大学）；Alibaba Group（阿里巴巴集团）；University of Chinese Academy of Sciences（中国科学院大学）；Zhejiang University（浙江大学）
Toward Cooperative Driving in Mixed Traffic: An Adaptive Potential Game-Based Approach with Field Test Verification
State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（智能绿色车辆与移动国家重点实验室，清华大学）；State Key Lab of Intelligent Transportation System, School of Transportation Science and Engineering, Beihang University（智能交通运输系统实验室，北京航空航天大学交通科学与工程学院）
