A multiagent reinforcement learning algorithm with non-linear dynamics

Journal of Artificial Intelligence Research

Volumn 33, Issue , 2008, Pages 521-549

  Abdallah, Sherief ^a   Lesser, Victor ^b  

^a THE BRITISH UNIVERSITY IN DUBAI   (United Arab Emirates)

^b University of Massachusetts Amherst   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; DIFFERENTIAL EQUATIONS; DYNAMICS; FERTILIZERS; GAME THEORY; INTELLIGENT AGENTS; MACHINE LEARNING; MULTI AGENT SYSTEMS; REINFORCEMENT LEARNING;

DYNAMICS DIFFERENTIAL EQUATIONS; LEARNING AGENTS; MULTI-AGENT REINFORCEMENT LEARNING; MULTIAGENT REINFORCEMENT LEARNING ALGORITHM; NASH EQUILIBRIA; NON-LINEAR DYNAMICS; PIECEWISE LINEAR; STATE-OF-THE-ART ALGORITHMS;

LEARNING ALGORITHMS;

EID: 70350699723     PISSN: None     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.2628     Document Type: Article

References (21)

1. Abdallah, S., & Lesser, V. (2006). Learning the task allocation game. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 850-857.
2. Abdallah, S., & Lesser, V. (2007). Multiagent reinforcement learning and self-organization in a network of agents. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems.
3. Abdallah, S., & Lesser, V. (2008). Non-linear dynamics in multiagent reinforcement learning algorithms. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1321-1324.
4. Banerjee, B., & Peng, J. (2003). Adaptive policy gradient in multiagent learning. In Proceedings of the International Joint Conference on Autonomous Agents and Multi Agent Systems, pp. 686-692.
5. Banerjee, B., & Peng, J. (2007). Generalized multiagent learning with performance bound. Autonomous Agents and Multiagent Systems, 15(3), 281-312.
6. Bowling, M. (2004). Convergence and no-regret in multiagent learning. Tech. rep., University of Alberta.
7. Bowling, M. (2005). Convergence and no-regret in multiagent learning. In Proceedings of the Annual Conference on Advances in Neural Information Processing Systems, pp. 209-216.
8. Bowling, M., & Veloso, M. (2002). Multiagent learning using a variable learning rate. Artificial Intelligence, 136(2), 215-250.
9. Boyan, J. A., & Littman, M. L. (1994). Packet routing in dynamically changing networks: A reinforcement learning approach. In Proceedings of the Annual Conference on Advances in Neural Information Processing Systems, pp. 671-678.
10. Claus, C., & Boutilier, C. (1998). The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the National Conference on Artificial intelligence/Innovative Applications of Artificial Intelligence, pp. 746-752.
11. Conitzer, V., & Sandholm, T. (2007). AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents. Machine Learning, 67(1-2), 23-43.
12. Dutta, P. S., Jennings, N. R., & Moreau, L. (2005). Cooperative information sharing to improve distributed learning in multi-agent systems. Journal of Artificial Intelligence Research, 24, 407-463.
13. Hu, J., & Wellman, M. P. (2003). Nash Q-learning for general-sum stochastic games. Journal of Machine Learning Research, 4, 1039-1069.
14. Khalil, H. K. (2002). Nonlinear Systems. Prentice-Hall, Upper Saddle River, NJ, USA.
15. Littman, M. (2001). Value-function reinforcement learning in Markov games. Cognitive Systems Research, 2(12), 55-66.
16. Peshkin, L., Kim, K.-E., Meuleau, N., & Kaelbling, L. P. (2000). Learning to cooperate via policy search. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp. 307-314.
17. Singh, S., Kearns, M., & Mansour, Y. (2000). Nash convergence of gradient dynamics in generalsum games. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp. 541-548.
18. Sutton, R., & Barto, A. (1999). Reinforcement Learning: An Introduction. MIT Press.
19. Tuyls, K., 't Hoen, P. J., & Vanschoenwinkel, B. (2006). An evolutionary dynamical analysis of multi-agent learning in iterated games. Autonomous Agents and Multi-Agent Systems, 12(1), 115-153.
20. Wang, X., & Sandholm, T. (2003). Reinforcement learning to play an optimal Nash equilibrium in team Markov games. In Proceedings of the Annual Conference on Advances in Neural Information Processing Systems, pp. 1571-1578.
21. Zinkevich, M. (2003). Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the International Conference on Machine Learning, pp. 928-936.