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Title: Applications of probabilistic inference to planning & reinforcement learning
Author: Furmston, T. J.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2013
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Optimal control is a profound and fascinating subject that regularly attracts interest from numerous scien- tific disciplines, including both pure and applied Mathematics, Computer Science, Artificial Intelligence, Psychology, Neuroscience and Economics. In 1960 Rudolf Kalman discovered that there exists a dual- ity between the problems of filtering and optimal control in linear systems [84]. This is now regarded as a seminal piece of work and it has since motivated a large amount of research into the discovery of similar dualities between optimal control and statistical inference. This is especially true of recent years where there has been much research into recasting problems of optimal control into problems of statis- tical/approximate inference. Broadly speaking this is the perspective that we take in this work and in particular we present various applications of methods from the fields of statistical/approximate inference to optimal control, planning and Reinforcement Learning. Some of the methods would be more accu- rately described to originate from other fields of research, such as the dual decomposition techniques used in chapter(5) which originate from convex optimisation. However, the original motivation for the application of these techniques was from the field of approximate inference. The study of dualities be- tween optimal control and statistical inference has been a subject of research for over 50 years and we do not claim to encompass the entire subject. Instead, we present what we consider to be a range of interesting and novel applications from this field of research
Supervisor: Barber, D. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available