Multi-Agent Common Knowledge Reinforcement Learning
暂无分享,去创建一个
Shimon Whiteson | Philip H. S. Torr | Jakob N. Foerster | Christian A. Schröder de Witt | Gregory Farquhar | Wendelin Boehmer | Gregory Farquhar | Wendelin Böhmer | C. S. D. Witt | Shimon Whiteson
[1] Kazuyuki Aihara,et al. Multi-agent reinforcement learning algorithm to handle beliefs of other agents' policies and embedded beliefs , 2006, AAMAS '06.
[2] Erfu Yang,et al. Multiagent Reinforcement Learning for Multi-Robot Systems: A Survey , 2004 .
[3] Ariel Rubinstein,et al. A Course in Game Theory , 1995 .
[4] Hung Manh La,et al. Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage , 2018, ArXiv.
[5] Varun Jampani,et al. Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[6] Rohit Parikh,et al. Probabilistic knowledge and probabilistic common knowledge , 1991 .
[7] Tom Schaul,et al. StarCraft II: A New Challenge for Reinforcement Learning , 2017, ArXiv.
[8] Jonathan P. How,et al. Deep Decentralized Multi-task Multi-Agent RL under Partial Observability , 2017 .
[9] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[10] Noa Agmon,et al. Ad hoc teamwork for leading a flock , 2013, AAMAS.
[11] F. Heider,et al. An experimental study of apparent behavior , 1944 .
[12] Emil Gustavsson,et al. Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence , 2016, ArXiv.
[13] Jonathan P. How,et al. Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[14] Nicolas Usunier,et al. Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks , 2016, ArXiv.
[15] A. Rubinstein. The Electronic Mail Game: Strategic Behavior Under "Almost Common Knowledge" , 1989 .
[16] Peter Stone,et al. Reasoning about Hypothetical Agent Behaviours and their Parameters , 2017, AAMAS.
[17] Jonathan P. How,et al. Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability , 2017, ICML.
[18] Yishay Mansour,et al. Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.
[19] Nikos A. Vlassis,et al. Sparse cooperative Q-learning , 2004, ICML.
[20] Shobha Venkataraman,et al. Context-specific multiagent coordination and planning with factored MDPs , 2002, AAAI/IAAI.
[21] John N. Tsitsiklis,et al. Actor-Critic Algorithms , 1999, NIPS.
[22] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[23] John K. Tsotsos,et al. Agreeing to cross: How drivers and pedestrians communicate , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).
[24] Jun Wang,et al. Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games , 2017, ArXiv.
[25] Ronald L. Rivest,et al. The Optimality of Correlated Sampling , 2016, Electron. Colloquium Comput. Complex..
[26] Boi Faltings,et al. Decentralized Anti-coordination Through Multi-agent Learning , 2013, J. Artif. Intell. Res..
[27] Piotr J. Gmytrasiewicz,et al. Interactive POMDPs with finite-state models of other agents , 2017, Autonomous Agents and Multi-Agent Systems.
[28] Rob Fergus,et al. Learning Multiagent Communication with Backpropagation , 2016, NIPS.
[29] Sridhar Mahadevan,et al. Hierarchical multi-agent reinforcement learning , 2001, AGENTS '01.
[30] Bikramjit Banerjee,et al. Multi-agent reinforcement learning as a rehearsal for decentralized planning , 2016, Neurocomputing.
[31] Wenwu Yu,et al. An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination , 2012, IEEE Transactions on Industrial Informatics.
[32] Dilek Z. Hakkani-Tür,et al. Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learning , 2017, ArXiv.
[33] Mykel J. Kochenderfer,et al. Cooperative Multi-agent Control Using Deep Reinforcement Learning , 2017, AAMAS Workshops.
[34] Chunhui Zhao,et al. Multi-vehicle Flocking Control with Deep Deterministic Policy Gradient Method , 2018, 2018 IEEE 14th International Conference on Control and Automation (ICCA).
[35] Thomas Holenstein,et al. Parallel repetition: simplifications and the no-signaling case , 2007, STOC '07.
[36] Guy Lever,et al. Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward , 2018, AAMAS.
[37] Steven Pinker,et al. The psychology of coordination and common knowledge. , 2014, Journal of personality and social psychology.
[38] Stephen F. Smith,et al. A few good agents: multi-agent social learning , 2008, AAMAS.
[39] Florian Richoux,et al. TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games , 2016, ArXiv.
[40] Ashutosh Nayyar,et al. Decentralized Stochastic Control with Partial History Sharing: A Common Information Approach , 2012, IEEE Transactions on Automatic Control.
[41] R. Aumann. Subjectivity and Correlation in Randomized Strategies , 1974 .
[42] Craig Boutilier,et al. Sequential Optimality and Coordination in Multiagent Systems , 1999, IJCAI.
[43] Peter Stone,et al. Three years of the RoboCup standard platform league drop-in player competition , 2016, Autonomous Agents and Multi-Agent Systems.
[44] Wojciech Zaremba,et al. Domain randomization for transferring deep neural networks from simulation to the real world , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[45] Tom Schaul,et al. FeUdal Networks for Hierarchical Reinforcement Learning , 2017, ICML.
[46] Joseph Y. Halpern,et al. Knowledge and common knowledge in a distributed environment , 1984, JACM.
[47] Craig Boutilier,et al. The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems , 1998, AAAI/IAAI.
[48] Shimon Whiteson,et al. Learning to Communicate with Deep Multi-Agent Reinforcement Learning , 2016, NIPS.
[49] Ronen I. Brafman,et al. Learning to Coordinate Efficiently: A Model-based Approach , 2003, J. Artif. Intell. Res..
[50] Haitham Bou-Ammar,et al. Learning to Communicate Implicitly by Actions , 2018, AAAI.
[51] Peng Peng,et al. Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games , 2017, 1703.10069.
[52] Shimon Whiteson,et al. Counterfactual Multi-Agent Policy Gradients , 2017, AAAI.
[53] Yi Wu,et al. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments , 2017, NIPS.
[54] Sarit Kraus,et al. Empirical evaluation of ad hoc teamwork in the pursuit domain , 2011, AAMAS.
[55] Nikos A. Vlassis,et al. Optimal and Approximate Q-value Functions for Decentralized POMDPs , 2008, J. Artif. Intell. Res..
[56] Shimon Whiteson,et al. The StarCraft Multi-Agent Challenge , 2019, AAMAS.
[57] Shimon Whiteson,et al. QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning , 2018, ICML.
[58] Leslie Pack Kaelbling,et al. Planning with macro-actions in decentralized POMDPs , 2014, AAMAS.
[59] Madhav V. Marathe,et al. Collective action through common knowledge using a facebook model , 2014, AAMAS.
[60] Thomas G. Dietterich. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition , 1999, J. Artif. Intell. Res..
[61] 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).
[62] Carlos Guestrin,et al. Multiagent Planning with Factored MDPs , 2001, NIPS.
[63] Shimon Whiteson,et al. Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning , 2017, ICML.