Learning in One-Shot Strategic Form Games
We propose a machine learning approach to action prediction in one-shot games. In contrast to the huge literature on learning in games where an agent’s model is deduced from its previous actions in a multi-stage game, we propose the idea of inferring correlations between agents’ actions in different one-shot games in order to predict an agent’s action in a game which she did not play yet. We define the approach and show, using real data obtained in experiments with human subjects, the feasibility of this approach. Furthermore, we demonstrate that this method can be used to increase payoffs of an adequately informed agent. This is, to the best of our knowledge, the first proposed and tested approach for learning in one-shot games, which is the most basic form of multi-agent interaction.
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- 1.Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: A survey. Journal of AI Research 4, 237–285 (1996)Google Scholar
- 2.Erev, I., Roth, A.E.: Predicting how people play games: Reinforcement learning in games with unique strategy equilibrium. American Economic Review 88, 848–881 (1998)Google Scholar
- 3.Claus, C., Boutilier, C.: The dynamics of reinforcement learning in cooperative multi-agent systems. In: Proc. Workshop on Multi-Agent Learning, pp. 602–608 (1997)Google Scholar
- 5.Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Proc. 11th ICML, pp. 157–163 (1994)Google Scholar
- 6.Hu, J., Wellman, M.: Multi-agent reinforcement learning: Theoretical framework and an algorithms. In: Proc. 15th ICML (1998)Google Scholar
- 7.Brafman, R.I., Tennenholtz, M.: R-max – a general polynomial time algorithm for near-optimal reinforcement learning. In: IJCAI 2001 (2001)Google Scholar
- 9.Kagel, J.H., Roth, A.: The Handbook of Experimental Economics. Princeton University Press, Princeton (1995)Google Scholar
- 13.Gal, Y., Pfeffer, A., Marzo, F., Grosz, B.J.: Learning social preferences in games. In: Proc. of AAAI 2004, pp. 226–231 (2004)Google Scholar
- 14.Stahl, D.O.: Population rule learning in symmetric normal-form games: theory and evidence. Journal of Economic Behavior and Organization 1304, 1–14 (2001)Google Scholar
- 16.Fudenberg, D., Tirole, J.: Game Theory. MIT Press, Cambridge (1991)Google Scholar
- 17.Nudelman, E., Wortman, J., Shoham, Y., Leyton-Brown, K.: Run the gamut: A comprehensive approach to evaluating game-theoretic algorithms. In: AAMAS 2004, pp. 880–887 (2004)Google Scholar
- 19.Erev, I., Roth, A., Slonim, R., Barron, G.: Learning and equilibrium as useful approximations: accuracy of prediction on randomly selected constant sum games (2006)Google Scholar