PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Practical and Conceptual Framework for Learning in Control
Marc Deisenroth and Carl Edward Rasmussen
(2010) Technical Report. University of Washington.

Abstract

We propose a fully Bayesian approach for efficient reinforcement learning (RL) in Markov decision processes with continuous-valued state and action spaces when no expert knowledge is available. Our framework is based on well-established ideas from statistics and machine learning and learns fast since it carefully models, quantifies, and incorporates available knowledge when making decisions. The key ingredient of our framework is a probabilistic model, which is implemented using a Gaussian process (GP), a distribution over functions. In the context of dynamic systems, the GP models the transition function. By considering all plausible transition functions simultaneously, we reduce model bias, a problem that frequently occurs when deterministic models are used. Due to its generality and efficiency, our RL framework can be considered a conceptual and practical approach to learning models and controllers when expert knowledge is difficult to obtain or simply not available, which makes system identification hard.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6978
Deposited By:Marc Deisenroth
Deposited On:09 July 2010