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This paper addresses the problem of multi-agent inverse reinforcement learning (MIRL) in a two-player general-sum stochastic game framework.
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Subjectivity and correlation in randomized strategies
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Hu, J., and Wellman, M. P. (2003) · 2003
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Bayesian inverse reinforcement learning.
Ramachandran, D., and Amir, E. (2007) · 2007
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Lecture 4: Strategic form games: solution concepts correlated rationalizability
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Reinforced learning in market games
Piotrowski, E. W., Sładkowski, J., and Szczypińska, A. (2010) · 2010
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Inverse reinforcement learning with gaussian process
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Computational rationalization: The inverse equilibrium problem
Waugh, K., Ziebart, B., and Bagnell, J. (2011) · 2011
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Economics of the Welfare State
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Inverse reinforcement learning for decentralized non-cooperative multiagent systems
Reddy, T. S., Gopikrishna, V., Zaruba, G., and Huber, M. (2012) · 2012
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