Opponent modeling in deep reinforcement learning

33rd International Conference on Machine Learning, ICML 2016

Volumn 4, Issue , 2016, Pages 2675-2684

  He, He ^a   Boyd Graber, Jordan ^b   Kwok, Kevin ^c   Daume, Hal ^a  

^a UNIVERSITY OF MARYLAND   (United States)

^b UNIVERSITY OF COLORADO   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; MULTI AGENT SYSTEMS;

EXPLICIT MODELING; MIXTURE OF EXPERTS; MULTI-AGENT SETTING; OPPONENT MODELING; PARAMETERIZED; PROBABILISTIC MODELS; SOCCER GAMES;

REINFORCEMENT LEARNING;

EID: 84999027774     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Bard, Nolan, Johanson, Michael, Burch, Neil, and Bowling, Michael. Online implicit agent modelling. In Proceedings of International Conference on Autonomous Agents and Multiagent Systems, 2013.
2. Billings, Darse, Papp, Denis, Schaeffer, Jonathan, and Szafron, Duane. Opponent modeling in poker. In Association for the Advancement of Artificial Intelligence, 1998a.
3. Billings, Darse, Papp, Denis, Schaeffer, Jonathan, and Szafron, Duane. Opponent modeling in poker. In Association for the Advancement of Artificial Intelligence, 1998b.
4. Boyd-Graber, Jordan, Satinoff, Brianna, He, He, and Daume ID, Hal. Besting the quiz master: Crowdsourcing incremental classification games. In Empirical Methods in Natural Language Processing, 2012.
5. Collins, Brian. Combining opponent modeling and model- based reinforcement learning in a two-player competitive game. Master's thesis, School of Informatics, University of Edinburgh, 2007.
6. Davidson, Aaron. Using artifical neural networks to model opponents in texas hold'em. CMPUT 499 - Research Project Review, 1999. URL http://www.spaz.ca/aaron/poker/nnpoker.pdf.
7. Duchi, John, Hazan, Elad, and Singer, Yoram. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 2011.
8. Eigen, David, Ranzato, Marc'Aurelio, and Sutskever, Ilya. Learning factored representations in a deep mixture of experts. In ICLR Workshop, 2014.
9. Foerster, Jakob N., Assael, Yannis M., de Freitas, Nando, and Whiteson, Shimon. Learning to communicate to solve riddles with deep distributed recurrent q-networks. Arxiv:1602.02672, 2016.
10. Ganzfried, Sam and Sandholm, Tuomas. Game theory- based opponent modeling in large imperfect-information games. In Proceedings of International Conference on Autonomous Agents and Multiagent Systems, 2011.
11. Hausknecht, Matthew and Stone, Peter. Deep recurrent q-learning for partially observable MDPs. Arxiv: 1507.06527, 2015.
12. Jacobs, Robert A., Jordan, Michael I., Nowlan, Steven J., and Hinton, Geoffrey E. Adaptive mixtures of local experts. Neural Computation, 3(1):79-87, 1991.
13. Littman, Michael L. Markov games as a framework for multi-agent reinforcement learning. In Proceedings of the International Conference of Machine Learning, 1994.
14. Lockett, Alan J., Chen, Charles L., and Miikkulainen, Risto. Evolving explicit opponent models in game playing. In Proceeedings of the Genetic and Evolutionary Computation Conference, 2007.
15. Mnih, Volodymyr, Heess, Nicolas, Graves, Alex, and Kavukcuoglu, Koray. Recurrent models of visual attention. In Proceedings of Advances in Neural Information Processing Systems, 2014.
16. Mnih, Volodymyr, Kavukcuoglu, Koray, Silver, David, Rusu, Andrei A., Veness, Joel, Bellemare, Marc G., Graves, Alex, Riedmiller, Martin, Fidjeland, Andreas K., Ostrovski, Georg, Petersen, Stig, Beattie, Charles, Sadik, Amir, Antonoglou, Ioannis, King, Helen, Kumaran, Dhar- shan, Wierstra, Daan, Legg, Shane, and Hassabis, Demis. Human-level control through deep reinforcement learning. Nature, 518(7540):529-533, 02 2015. URL http://dx.doi.org/10.1038/naturel4236.
17. Richards, Mark and Amir, Eyal. Opponent modeling in Scrabble. In International Joint Conference on Artificial Intelligence, 2007.
18. Rubin, Jonathan and Watson, Ian. On combining decisions from multiple expert imitators for performance. In International Joint Conference on Artificial Intelligence, 2011.
19. Schadd, Frederik, Bakkes, Er, and Spronck, Pieter. Opponent modeling in real-time strategy games. In Proceedings of the GAME-ON 2007, pp. 61-68, 2007.
20. Southey, Finnegan, Bowling, Michael, Larson, Bryce, Pic- cione, Carmelo, Burch, Neil, Billings, Darse, and Rayner, Chris. Bayes' bluff: Opponent modelling in poker. In Proceedings of Uncertainty in Artificial Intelligence, 2005.
21. Sutton, Richard S and Barto, Andrew G. Reinforcement learning: An introduction, volume 1. MIT Press Cambridge, 1998.
22. Tampuu, Ardi, Matiisen, Tambet, Kodelja, Dorian, Ku-zovkin, Ilya, Korjus, Kristjan, Aru, Juhan, Aru, Jaan, and Vicente, Raul. Multiagent cooperation and competition with deep reinforcement learning. ArXiv: 1511.08779, 2015.
23. Uther, William and Veloso, Manuela. Adversarial reinforcement learning. Technical Report CMU-CS-03-107, School of Computer Science, Carnegie Mellon University, 2003.
24. Watkins, Christopher J. C. H. and Dayan, Peter. Q-learning. Machine Learning, 8(3-4):279-292, 1992.
25. Zhang, Marvin, McCarthy, Zoe, Finn, Chelsea, Levine, Sergey, and Abbeel, Pieter. Learning deep neural network policies with continuous memory states. ArXiv: 1507.01273, 2015.