Imitation learning with a value-based prior

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The goal of imitation learning is for an apprentice to learn how to behave in a stochastic environment by observing a mentor demonstrating the correct behavior. Accurate prior knowledge about the correct behavior can reduce the need for demonstrations from the mentor. We present a novel approach to encoding prior knowledge about the correct behavior, where we assume that this prior knowledge takes the form of a Markov Decision Process (MDP) that is used by the apprentice as a rough and imperfect model of the mentor's behavior. Specifically, taking a Bayesian approach, we treat the value of a policy in this modeling MDP as the log prior probability of the policy. In other words, we assume a priori that the mentor's behavior is likely to be a high-value policy in the modeling MDP, though quite possibly different from the optimal policy. We describe an efficient algorithm that, given a modeling MDP and a set of demonstrations by a mentor, provably converges to a stationary point of the log posterior of the mentor's policy, where the posterior is computed with respect to the "value-based" prior. We also present empirical evidence that this prior does in fact speed learning of the mentor's policy, and is an improvement in our experiments over similar previous methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007
Pages384-391
Number of pages8
StatePublished - Dec 1 2007
Event23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007 - Vancouver, BC, Canada
Duration: Jul 19 2007Jul 22 2007

Publication series

NameProceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007

Conference

Conference23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007
CountryCanada
CityVancouver, BC
Period7/19/077/22/07

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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