Convergence Analysis of Graphical Game-Based Nash Q−Learning using the Interaction Detection Signal of N−Step Return
暂无分享，去创建一个
[1] K. Zhang,et al. On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning , 2021, ICML.
[2] Tuomas Sandholm,et al. Subgame solving without common knowledge , 2021, NeurIPS.
[3] Yaodong Yang,et al. An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective , 2020, ArXiv.
[4] Hongyuan Zha,et al. Learning structured communication for multi-agent reinforcement learning , 2020, Autonomous Agents and Multi-Agent Systems.
[5] Yujing Hu,et al. Multi-Agent Game Abstraction via Graph Attention Neural Network , 2019, AAAI.
[6] Kristoffer Arnsfelt Hansen,et al. On the Computational Complexity of Decision Problems About Multi-player Nash Equilibria , 2019, Theory of Computing Systems.
[7] S. Whiteson,et al. Deep Coordination Graphs , 2019, ICML.
[8] Yung Yi,et al. QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning , 2019, ICML.
[9] Tuomas Sandholm,et al. Depth-Limited Solving for Imperfect-Information Games , 2018, NeurIPS.
[10] Shimon Whiteson,et al. QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning , 2018, ICML.
[11] Joel Z. Leibo,et al. Value-Decomposition Networks For Cooperative Multi-Agent Learning , 2017, ArXiv.
[12] Paul Scerri,et al. Coordination of Large-Scale Multiagent Systems , 2005 .
[13] Michail G. Lagoudakis,et al. Coordinated Reinforcement Learning , 2002, ICML.