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2009
Springer

Active Learning for Reward Estimation in Inverse Reinforcement Learning

9 years 2 months ago
Active Learning for Reward Estimation in Inverse Reinforcement Learning
Abstract. Inverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, we introduce active learning for inverse reinforcement learning. We propose an algorithm that allows the agent to query the demonstrator for samples at specific states, instead of relying only on samples provided at “arbitrary” states. The purpose of our algorithm is to estimate the reward function with similar accuracy as other methods from the literature while reducing the amount of policy samples required from the expert. We also discuss the use of our algorithm in higher dimensional problems, using both Monte Carlo and gradient methods. We present illustrative results of our algorithm in several simulated examples of different complexities.
Manuel Lopes, Francisco S. Melo, Luis Montesano
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where PKDD
Authors Manuel Lopes, Francisco S. Melo, Luis Montesano
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