Lab Meeting February 14, 2011 (fish60): Feature Construction for Inverse Reinforcement Learning

Title: Feature Construction for Inverse Reinforcement Learning
Sergey Levine, Zoran Popović, Vladlen Koltun
NIPS 2010

Abstract:
The goal of inverse reinforcement learning is to find a reward function for a
Markov decision process, given example traces from its optimal policy. Current
IRL techniques generally rely on user-supplied features that form a concise basis
for the reward. We present an algorithm that instead constructs reward features
from a large collection of component features, by building logical conjunctions of
those component features that are relevant to the example policy. Given example
traces, the algorithm returns a reward function as well as the constructed features.

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