S. Airiau, S. Saha, and S. , Sen: Evolutionary Tournament-based Comparison of Learning and Non-learning Algorithm for Iterated games, Journal of Artificial Societies and Social Simulation, vol.10, issue.3, 2007.

P. Auer, N. Cesa-bianchi, and P. Fischer, Finite-time Analysis of the multi-armed bandit problem, Machine Learning, vol.47, issue.2/3, pp.235-256, 2002.
DOI : 10.1023/A:1013689704352

P. Auer, N. Cesa-bianchi, Y. Freund, and R. E. Schapire, The Nonstochastic Multiarmed Bandit Problem, SIAM Journal on Computing, vol.32, issue.1, pp.48-77, 2002.
DOI : 10.1137/S0097539701398375

B. Banerjee and J. Peng, Performance bounded reinforcement learning in strategic interactions, pp.2-7, 2004.

G. W. Brown, Iterative solution of games by Fictitious Play Activity Analysis of Production and Allocation, pp.374-376, 1951.

Y. H. Chang and L. P. Kaelbling, Hedged learning, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.121-128, 2005.
DOI : 10.1145/1102351.1102367

V. Conitzer and T. Sandholm, AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents, Machine Learning, vol.54, issue.1-2, pp.83-90, 2003.
DOI : 10.1007/s10994-006-0143-1

J. W. Crandall and M. A. Goodrich, Learning to compete, compromise, and cooperate in repeated general-sum games, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.161-168, 2005.
DOI : 10.1145/1102351.1102372

Y. Freund and R. E. Schapire, Adaptive Game Playing Using Multiplicative Weights, Games and Economic Behavior, vol.29, issue.1-2, pp.79-103, 1999.
DOI : 10.1006/game.1999.0738

N. Littlestone and M. K. Warmuth, The Weighted Majority Algorithm, Information and Computation, vol.108, issue.2, pp.212-261, 1994.
DOI : 10.1006/inco.1994.1009

M. Littman, Markov games as a framework for multi-agent reinforcement learning, pp.157-163, 1994.
DOI : 10.1016/B978-1-55860-335-6.50027-1

M. Littman, Friend-or-Foe Q-learning in General-Sum Games, ICML, pp.322-328, 2001.

J. Nachbar and W. , Zame: Non-computable strategies and discounted repeated games, Economic Theory, vol.8, issue.1, pp.103-122, 1996.

E. Nudelman, J. Wortman, Y. Shoham, and K. Leyton-brown, Run the GAMUT: a comprehensive approach to evaluating game-theoretic algorithms, AMAAS, pp.880-887, 2004.

R. Powers and Y. Shoham, New criteria and a new algorithm for learning in multiagent systems, NIPS, 2004.

R. Powers and Y. Shoham, Learning against opponents with bounded memory, IJCAI, pp.817-822, 2005.

D. Pucci and N. Megiddo, expert advice in reactive environments, Journal of ACM, vol.53, 2006.

J. Robinson, An Iterative Method of Solving a Game, The Annals of Mathematics, vol.54, issue.2, pp.296-301, 1951.
DOI : 10.2307/1969530

T. Sandholm and R. , Crites: Multiagent Reinforcement Learning in the Iterated Prisoner's Dilemma, Biosystems, vol.7, issue.12, pp.147-166, 1996.

Y. Shoham, R. Powers, and T. Grenager, If multi-agent learning is the answer, what is the question?, Artificial Intelligence, vol.171, issue.7, pp.365-377, 2007.
DOI : 10.1016/j.artint.2006.02.006

J. L. Stimpson and M. A. Goodrich, Learning to cooperate in a social dilemma: a satisficing approach to bargaining, ICML, pp.728-735, 2003.

P. Stone and M. Littman, Implicit Negotiation in Repeated Games, pp.96-105, 2001.

V. Vovk, A Game of Prediction with Expert Advice, Journal of Computer and System Sciences, vol.56, issue.2, pp.153-173, 1998.
DOI : 10.1006/jcss.1997.1556

E. Zawadzki, Multiagent Learning and Empirical Methods, 2005.