ar X iv : 1 21 1 . 59 01 v 1 [ st at . M L ] 2 6 N ov 2 01 2 BAYESIAN LEARNING OF NOISY MARKOV DECISION PROCESSES
نویسنده
چکیده
We consider the inverse reinforcement learning problem, that is, the problem of learning from, and then predicting or mimicking a controller based on state/action data. We propose a statistical model for such data, derived from the structure of a Markov decision process. Adopting a Bayesian approach to inference, we show how latent variables of the model can be estimated, and how predictions about actions can be made, in a unified framework. A new Markov chain Monte Carlo (MCMC) sampler is devised for simulation from the posterior distribution. This step includes a parameter expansion step, which is shown to be essential for good convergence properties of the MCMC sampler. As an illustration, the method is applied to learning a human controller.
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تاریخ انتشار 2012