暂无分享,去创建一个
Naira Hovakimyan | Aditya Gahlawat | Lin Song | Evangelos A. Theodorou | Neng Wan | Evangelos A. Theodorou | E. Theodorou | N. Hovakimyan | Aditya Gahlawat | Lin Song | Neng Wan
[1] Emanuel Todorov,et al. Compositionality of optimal control laws , 2009, NIPS.
[2] Tamer Basar,et al. Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents , 2018, ICML.
[3] Siddharth Mayya,et al. The Robotarium: Globally Impactful Opportunities, Challenges, and Lessons Learned in Remote-Access, Distributed Control of Multirobot Systems , 2020, IEEE Control Systems.
[4] David Silver,et al. A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning , 2017, NIPS.
[5] E. Todorov,et al. A UNIFIED THEORY OF LINEARLY SOLVABLE OPTIMAL CONTROL , 2012 .
[6] Tamer Basar,et al. A game-theoretic formulation of multi-agent resource allocation , 2000, AGENTS '00.
[7] E. Todorov,et al. Linearly Solvable Optimal Control , 2013 .
[8] Liu Ying,et al. Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning , 2017, ITSC 2017.
[9] Milind Tambe,et al. Taking DCOP to the real world: efficient complete solutions for distributed multi-event scheduling , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..
[10] Stefan Schaal,et al. Reinforcement learning of motor skills in high dimensions: A path integral approach , 2010, 2010 IEEE International Conference on Robotics and Automation.
[11] Manuela M. Veloso,et al. Multiagent Systems: A Survey from a Machine Learning Perspective , 2000, Auton. Robots.
[12] Michael P. Wellman,et al. Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm , 1998, ICML.
[13] Jonathan P. How,et al. Decentralized control of partially observable Markov decision processes , 2015, 52nd IEEE Conference on Decision and Control.
[14] Pravin Varaiya,et al. Stochastic Systems: Estimation, Identification, and Adaptive Control , 1986 .
[15] Lei Liu,et al. Intelligent traffic light control using distributed multi-agent Q learning , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).
[16] Michael L. Littman,et al. Markov Games as a Framework for Multi-Agent Reinforcement Learning , 1994, ICML.
[17] 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).
[18] Emanuel Todorov,et al. Efficient computation of optimal actions , 2009, Proceedings of the National Academy of Sciences.
[19] John N. Tsitsiklis,et al. Neuro-dynamic programming: an overview , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.
[20] Tamer Basar,et al. Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms , 2019, Handbook of Reinforcement Learning and Control.
[21] John N. Tsitsiklis,et al. A survey of computational complexity results in systems and control , 2000, Autom..
[22] Yishay Mansour,et al. Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.
[23] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[24] Cheng-Lin Liu,et al. Average-Consensus Tracking of Sensor Network via Distributed Coordination Control of Heterogeneous Multi-Agent Systems , 2019, IEEE Control Systems Letters.
[25] Stefan Schaal,et al. A Generalized Path Integral Control Approach to Reinforcement Learning , 2010, J. Mach. Learn. Res..
[26] Jonathan P. How,et al. Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions , 2017, Int. J. Robotics Res..
[27] François Charpillet,et al. A heuristic approach for solving decentralized-POMDP: assessment on the pursuit problem , 2002, SAC '02.
[28] Frank L. Lewis,et al. Reinforcement Learning and Approximate Dynamic Programming for Feedback Control , 2012 .
[29] Warrren B Powell,et al. A review of stochastic algorithms with continuous value function approximation and some new approximate policy iteration algorithms for multidimensional continuous applications , 2011 .
[30] Evangelos A. Theodorou,et al. Sample Efficient Path Integral Control under Uncertainty , 2015, NIPS.