Enhancing Nash Q-learning and Team Q-learning mechanisms by using bottlenecks
暂无分享,去创建一个
[1] Thomas G. Dietterich. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition , 1999, J. Artif. Intell. Res..
[2] Sean Luke,et al. Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.
[3] Yoav Shoham,et al. Multi-Agent Reinforcement Learning:a critical survey , 2003 .
[4] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[5] Bart De Schutter,et al. A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[6] Alicia P. Wolfe,et al. Identifying useful subgoals in reinforcement learning by local graph partitioning , 2005, ICML.
[7] Peter Stone,et al. Multiagent learning is not the answer. It is the question , 2007, Artif. Intell..
[8] Manuel Graña,et al. Cooperative Multi-Agent Reinforcement Learning for Multi-Component Robotic Systems: guidelines for future research , 2011, Paladyn J. Behav. Robotics.
[9] Andrew G. Barto,et al. Skill Characterization Based on Betweenness , 2008, NIPS.
[10] Guillaume J. Laurent,et al. Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems , 2012, The Knowledge Engineering Review.
[11] Michael H. Bowling,et al. Convergence Problems of General-Sum Multiagent Reinforcement Learning , 2000, ICML.
[12] Ronald E. Parr,et al. Hierarchical control and learning for markov decision processes , 1998 .
[13] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[14] Peter Stone,et al. Layered Learning in Multiagent Systems , 1997, AAAI/IAAI.
[15] Yoav Shoham,et al. If multi-agent learning is the answer, what is the question? , 2007, Artif. Intell..
[16] Doina Precup,et al. Temporal abstraction in reinforcement learning , 2000, ICML 2000.
[17] Bart De Schutter,et al. Multiagent Reinforcement Learning with Adaptive State Focus , 2005, BNAIC.
[18] Sridhar Mahadevan,et al. Hierarchical multi-agent reinforcement learning , 2001, AGENTS '01.
[19] Michael L. Littman,et al. Value-function reinforcement learning in Markov games , 2001, Cognitive Systems Research.
[20] Andrew G. Barto,et al. Using relative novelty to identify useful temporal abstractions in reinforcement learning , 2004, ICML.
[21] Keith B. Hall,et al. Correlated Q-Learning , 2003, ICML.
[22] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[23] Reinaldo A. C. Bianchi,et al. Multi-agent Multi-objective Learning Using Heuristically Accelerated Reinforcement Learning , 2012, 2012 Brazilian Robotics Symposium and Latin American Robotics Symposium.
[24] Victor Lesser,et al. Scaling multi-agent learning in complex environments , 2011 .
[25] Shie Mannor,et al. Dynamic abstraction in reinforcement learning via clustering , 2004, ICML.
[26] Shie Mannor,et al. Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning , 2002, ECML.
[27] Michael P. Wellman,et al. Nash Q-Learning for General-Sum Stochastic Games , 2003, J. Mach. Learn. Res..
[28] Erfu Yang,et al. Multiagent Reinforcement Learning for Multi-Robot Systems: A Survey , 2004 .
[29] Csaba Szepesvári,et al. A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms , 1999, Neural Computation.
[30] Erfu Yang,et al. A Survey on Multiagent Reinforcement Learning Towards Multi-Robot Systems , 2005, CIG.
[31] Von-Wun Soo,et al. AUTOMATIC COMPLEXITY REDUCTION IN REINFORCEMENT LEARNING , 2010, Comput. Intell..
[32] Sridhar Mahadevan,et al. A multiagent reinforcement learning algorithm by dynamically merging markov decision processes , 2002, AAMAS '02.
[33] J. Filar,et al. Competitive Markov Decision Processes , 1996 .
[34] Sridhar Mahadevan,et al. Learning to Take Concurrent Actions , 2002, NIPS.
[35] Aram Galstyan,et al. Continuous strategy replicator dynamics for multi-agent Q-learning , 2009, Autonomous Agents and Multi-Agent Systems.
[36] Bart De Schutter,et al. Multi-agent Reinforcement Learning: An Overview , 2010 .
[37] Yong Duan,et al. A multi-agent reinforcement learning approach to robot soccer , 2012, Artificial Intelligence Review.
[38] Faruk Polat,et al. A layered approach to learning coordination knowledge in multiagent environments , 2007, Applied Intelligence.
[39] Sridhar Mahadevan,et al. Recent Advances in Hierarchical Reinforcement Learning , 2003, Discret. Event Dyn. Syst..
[40] M. Ghavamzadeh,et al. Hierarchical reinforcement learning in continuous state and multi-agent environments , 2005 .
[41] Michael P. Wellman,et al. Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm , 1998, ICML.