| | |
Summary: Bayesian Inverse Reinforcement Learning
Deepak Ramachandran
Computer Science Dept.
University of Illinois at Urbana-Champaign
Urbana, IL 61801
Eyal Amir
Computer Science Dept.
University of Illinois at Urbana-Champaign
Urbana, IL 61801
Abstract
Inverse Reinforcement Learning (IRL) is the prob-
lem of learning the reward function underlying a
Markov Decision Process given the dynamics of
the system and the behaviour of an expert. IRL
is motivated by situations where knowledge of the
rewards is a goal by itself (as in preference elici-
tation) and by the task of apprenticeship learning
(learning policies from an expert). In this paper
we show how to combine prior knowledge and evi-
dence from the expert's actions to derive a probabil-
|
