Multi-agent reinforcement learning: A survey

9th International Conference on Control, Automation, Robotics and Vision, 2006, ICARCV '06

Volumn , Issue , 2006, Pages

  Buşoniu, Lucian ^a   Babuška, Robert ^a   De Schutter, Bart ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Distributed control; Game theory; Multi agent systems; Reinforcement learning

Indexed keywords

ALGORITHMS; BEHAVIORAL RESEARCH; DISTRIBUTED PARAMETER CONTROL SYSTEMS; ECONOMICS; GAME THEORY; MULTI AGENT SYSTEMS; ROBOTICS; TELECOMMUNICATION;

DISTRIBUTED CONTROL; MULTI-AGENT LEARNING;

REINFORCEMENT LEARNING;

EID: 34547192059     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICARCV.2006.345353     Document Type: Conference Paper

References (37)

1. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. Cambridge, US: MIT Press, 1998.
2. Y. Shoham, R. Powers, and T. Grenager, "Multi-agent reinforcement learning: A critical survey," Computer Science Dept., Stanford University, California, US, Tech. Rep., 16 May 2003.
3. K. Tuyls and A. Nowé, "Evolutionary game theory and multi-agent reinforcement learning," The Knowledge Engineering Review, vol. 20, no. 1, pp. 63-90, 2005.
4. G. Chalkiadakis, "Multiagent reinforcement learning: Stochastic games with multiple learning players," Dept. of Computer Science, University of Toronto, Canada, Tech. Rep., 25 March 2003, URL: http://www.cs.toronto.edu/~gehalk/DepthReport/DepthReport.ps.
5. M. Bowling, "Multiagent learning in the presence of agents with limitations," Ph.D. dissertation, Computer Science Dept., Carnegie Mellon University, Pittsburgh, US, May 2003.
6. L. Panait and S. Luke, "Cooperative multi-agent learning: The state of the art," Autonomous Agents and Multi-Agent Systems, vol. 11, no. 3, pp. 387-434, November 2005.
7. C. J. C. H. Watkins and P. Dayan, "Technical note: Q-learning," Machine Learning, vol. 8, pp. 279-292, 1992.
8. M. Bowling and M. Veloso, "Multiagent learning using a variable learning rate," Artificial Intelligence, vol. 136, no. 2, pp. 215-250, 2002.
9. V. Conitzer and T. Sandholm, "AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents," in Proceedings Twentieth International Conference on Machine Learning (ICML-03), Washington, US, 21-24 August 2003, pp. 83-90.
10. M. Bowling, "Convergence and no-regret in multiagent learning," in Advances in Neural Information Processing Systems 17 (NIPS-04), Vancouver, Canada, 13-18 December 2004, pp. 209-216.
11. M. L. Littman, "Value-function reinforcement learning in Markov games," Journal of Cognitive Systems Research, vol. 2, pp. 55-66, 2001.
12. R. Powers and Y Shoham, "New criteria and a new algorithm for learning in multi-agent systems," in Advances in Neural Information Processing Systems 17 (NIPS-04), Vancouver, Canada, 2004, pp. 1089-1096.
13. J. Hu and M. P. Wellman, "Nash Q-learning for general-sum stochastic games," Journal of Machine Learning Research, vol. 4, pp. 1039-1069, 2003.
14. A. Greenwald and K. Hall, "Correlated-Q learning," in Proceedings Twentieth International Conference on Machine Learning (ICML-03), Washington, US, 21-24 August 2003, pp. 242-249.
15. M. Lauer and M. Riedmiller, "An algorithm for distributed reinforcement learning in cooperative multi-agent systems," in Proceedings Seventeenth International Conference on Machine Learning (ICML-00), Stanford University, US, 29 June - 2 July 2000, pp. 535-542.
16. C. Boutilier, "Planning, learning and coordination in multiagent decision processes," in Proceedings Sixth Conference on Theoretical Aspects of Rationality and Knowledge (TARK-96), De Zeeuwse Stromen, The Netherlands, 17-20 March 1996, pp. 195-210.
17. M. T. J. Spaan, N. Vlassis, and F. C. A. Groen, "High level coordination of agents based on multiagent Markov decision processes with roles," in Workshop on Cooperative Robotics, 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-02), Lausanne, Switzerland, 1 October 2002, pp. 66-73.
18. C. Guestrin, M. G. Lagoudakis, and R. Parr, "Coordinated reinforcement learning," in Proceedings Nineteenth International Conference on Machine Learning (ICML-02), Sydney, Australia, 8-12 July 2002, pp. 227-234.
19. J. R. Kok, M. T. J. Spaan, and N. Vlassis, "Non-communicative multi-robot coordination in dynamic environment," Robotics and Autonomous Systems, vol. 50, no. 2-3, pp. 99-114, 2005.
20. N. Vlassis, "A concise introduction to multiagent systems and distributed AI," University of Amsterdam, The Netherlands, Tech. Rep., September 2003, URL: http://www.science.uva.nl/~vlassis/cimasdai/cimasdai.pdf.
21. F. Fischer, M. Rovatsos, and G. Weiss, "Hierarchical reinforcement learning in communication-mediated multiagent coordination," in Proceedings 3rd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-04), New York, US, 19-23 August 2004, pp. 1334-1335.
22. C. Claus and C. Boutilier, "The dynamics of reinforcement learning in cooperative multiagent systems," in Proceedings 15th National Conference on Artificial Intelligence and 10th Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAI-98), Madison, US, 26-30 July 1998, pp. 746-752.
23. S. Kapetanakis and D. Kudenko, "Reinforcement learning of coordination in cooperative multi-agent systems," in Proceedings 18th National Conference on Artificial Intelligence and 14th Conference on Innovative Applications of Artificial Intelligence (AAAI/IAAl-02), Menlo Park, US, 28 July - 1 August 2002, pp. 326-331.
24. X. Wang and T. Sandholm, "Reinforcement learning to play an optimal Nash equilibrium in team Markov games," in Advances in Neural. Information Processing Systems 15 (NIPS-02), Vancouver, Canada, 9-14 December 2002, pp. 1571-1578.
25. D. Carmel and S. Markovitch, "Opponent modeling in multi-agent systems," in Adaptation and Learning in Multi-Agent Systems, G. Weiß and S. Sen, Eds. Springer Verlag, 1996, pp. 40-52.
26. G. Tesauro, "Extending Q-leaming to general adaptive multi-agent systems," in Advances in Neural Information Processing Systems 16 (NIPS-03), Vancouver and Whistler, Canada, 8-13 December 2003.
27. S. Singh, M. Kearns, and Y. Mansour, "Nash convergence of gradient dynamics in general-sum games," in Proceedings 16th Conference on Uncertainty in Artificial Intelligence (UAI-00), San Francisco, US, 30 June - 3 July 2000, pp. 541-548.
28. M. Zinkevich, "Online convex programming and generalized infinitesimal gradient ascent," in Proceedings Twentieth International Conference on Machine Learning (ICML-03), Washington, US, 21-24 August 2003, pp. 928-936.
29. S. Sen, M. Sekaran, and J. Hale, "Learning to coordinate without sharing information," in Proceedings 12th National Conference on Artificial Intelligence (AAAI-94), Seattle, US, 31 July - 4 August 1994, pp. 426431.
30. M. J. Mataric̀, "Learning in multi-robot systems," in Adaptation and Learning in Multi-Agent Systems, G. Weiß and S. Sen, Eds. Springer Verlag, 1996, pp. 152-163.
31. R. H. Crites and A. G. Barto, "Improving elevator performance using reinforcement learning," in Advances in Neural Information Processing Systems, vol. 8, 1996, pp. 1017-1023.
32. V. Könönen, "Asymmetric multiagent reinforcement learning," in Proceedings IEEEAVTC International Conference on Intelligent Agent Technology (IAT-03), Halifax, Canada, 13-17 October 2003, pp. 336-342.
33. M. Weinberg and J. S. Rosenschein, "Best-response multiagent learning in non-stationary environments," in Proceedings 3rd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-04), New York, US, 19-23 August 2004, pp. 506-513.
34. B. Banerjee and J. Peng, "Adaptive policy gradient in multiagent learning," in Proceedings 2nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-03), Melbourne, Australia, 14-18 July 2003, pp. 686-692.
35. N. Suematsu and A. Hayashi, "A multiagent reinforcement learning algorithm using extended optimal response," in Proceedings 1st International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-02), Bologna, Italy, 15-19 July 2002, pp. 370-377.
36. J. Schmidhuber, "A general method for multi-agent reinforcement learning in unrestricted environments," in Working Notes AAAI Symposium on Adaptation, Co-evolution and Learning in Multiagent Systems, Stanford University, US, March 25-27 1996, pp. 84-87.
37. J. M. Vidal, "Learning in multiagent systems: An introduction from a game-theoretic perspective," in Adaptive Agents: Lecture Notes in Artificial Intelligence. Springer Verlag, August 2003, vol. 2636, pp. 202-215.