PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Reducing Model Bias in Reinforcement Learning
Marc Deisenroth and Carl Edward Rasmussen
In: Learning and Planning from Batch Time Series Data, 11 Dec 2010, Whistler, BC, Canada.

Abstract

Model bias is one of the main reasons why reinforcement learning (RL) algorithms often need so many trials to successfully learn a task. Model bias has been known for decades, but no general solution to this problem has yet been proposed. We shed some light on the challenges of learning models from data and propose learning probabilistic models to reduce model bias by faitfully incorporating the model's uncertainty into planning and policy learning

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7036
Deposited By:Marc Deisenroth
Deposited On:12 December 2010