The inability to communicate poses a major challenge to coordination in multi-agent reinforcement learning (MARL). Prior work has explored correlating local policies via shared randomness, sometimes in the form of a correlation device, as a mechanism to assist in decentralized decision-making. In contrast, this work introduces the first framework for training MARL agents to exploit shared quantum entanglement as a coordination resource, which permits a larger class of communication-free correlated policies than shared randomness alone. This is motivated by well-known results in quantum physics which posit that, for certain single-round cooperative games with no communication, shared quantum entanglement enables strategies that outperform those that only use shared randomness. In such cases, we say that there is quantum advantage. Our framework is based on a novel differentiable policy parameterization that enables optimization over quantum measurements, together with a novel policy architecture that decomposes joint policies into a quantum coordinator and decentralized local actors. To illustrate the effectiveness of our proposed method, we first show that we can learn, purely from experience, strategies that attain quantum advantage in single-round games that are treated as black box oracles. We then demonstrate how our machinery can learn policies with quantum advantage in an illustrative multi-agent sequential decision-making problem formulated as a decentralized partially observable Markov decision process (Dec-POMDP).
Reinforcement Learning Generation of 4-Qubits Entangled States
1 Departamento de F\' sica Te\'orica, Universidad Complutense, 28040 Madrid, Spain. 2 CCS-Center for Computational Simulation, Campus de Montegancedo UPM, 28660 Boadilla del Monte, Madrid, Spain.
HierRouter: Coordinated Routing of Specialized Large Language Models via Reinforcement Learning
David S. Hippocampus Department of Computer Science Cranberry-Lemon University（Cranberry-Lemon大学计算机科学系）；University of Southern California（南加州大学）；DEVCOM Army Research Office（陆军研究办公室）
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent
Chongqing University（重庆大学）；North Carolina State University（北卡罗来纳州立大学）；Oklahoma State University（俄克拉荷马州立大学）；DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）
CrystalGRPO: Target-Aligned and Coverage-Preserving Reinforcement Learning for Flow-Based Crystal Structure Prediction
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；North Carolina Battery Complexity, Autonomous Vehicle and Electrification (BATT CAVE) Research Center（北卡罗来纳州电池复杂性、自动驾驶与电气化（BATT CAVE）研究中心）
Coordinated Lane-Level Variable Speed Limits and Ramp Metering for Successive Weaving Segments Considering Merging/Diverging Risks: A Hybrid Model Predictive Control and Multi-Agent Reinforcement Learning Approach
Resilient Load Restoration in Microgrids Considering Mobile Energy Storage Fleets: A Deep Reinforcement Learning Approach
Interdisciplinary Graduate School, Nanyang Technological University Singapore（南洋理工大学跨学科研究生院（新加坡））；School of Electrical and Electronic Engineering, Nanyang Technological University Singapore（南洋理工大学电气与电子工程学院（新加坡））
Department of Computer Science, Cornell University（康奈尔大学计算机科学系）；Subharmonic Technologies Inc.（Subharmonic Technologies公司）；Department of Economics, Cornell University（康奈尔大学经济学系）；Department of Engineering Physics and Computation, Technical University of Munich（慕尼黑技术大学工程物理与计算系）；Department of Mechanical Engineering, University of Washington（华盛顿大学机械工程系）
Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling
Northeast Electric Power University（东北电力大学）；State Grid Zibo Power Supply Company（国网淄博供电公司）；State Grid Beijing Electric Power Company（国网北京市电力公司）；State Grid Jining Power Supply Company（国网济宁供电公司）
GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning
许多真实世界的场景涉及一组代理，他们必须协调他们的策略以实现共同的目标。以往的研究主要集中在分散控制上，以最大化共同的报酬，而很少考虑控制策略之间的协调，这在动态复杂环境中是至关重要的。在这项工作中，我们建议将联合团队策略分解为图形生成器和基于图形的协调策略，以实现代理之间的协调行为。图形生成器采用编码器-解码器框架，输出有向无环图（DAG）来捕获底层动态决策结构。我们还在图生成器中应用了DAGness-constrained和DAG depth-constrained优化来平衡效率和性能。基于图的协调策略利用生成的决策结构。同时训练图生成器和协调策略以最大化贴现收益。对协同高斯挤压、协同导航和Google Research Football的实验结果表明了该方法的优越性。
Joint Intrinsic Motivation for Coordinated Exploration in Multi-Agent Deep Reinforcement Learning
ECE Paris \& Sorbonne Universit\' e , CNRS,\ ISIR F-75005 Paris France；Sorbonne Universit\' e , CNRS, ISIR F-75005 Paris France；ECE Paris \& Sorbonne Universit\' e , CNRS,\ ISIR；Sorbonne Universit\' e , CNRS, ISIR
Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Microsoft（微软公司）
