A three-chapter monograph that uses Afriat's theorem and Bayesian revealed preference tests for inverse reinforcement learning, plus a passive Langevin dynamics algorithm for real-time reward reconstruction.
Finite-sample bounds for adaptive inverse reinforcement learn- ing using passive langevin dynamics
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Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization
A three-chapter monograph that uses Afriat's theorem and Bayesian revealed preference tests for inverse reinforcement learning, plus a passive Langevin dynamics algorithm for real-time reward reconstruction.