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Application of Statistical Estimation Theory, Adaptive Sensory Systems and Time Series Processing to Reinforcement Learning
Citation key Gruenwalder2009
Author Steffen Grünewälder
Year 2009
School Technische Universität Berlin
Abstract In this thesis three major topics of Reinforcement Learning (RL) are addressed: (1) The classical control and estimation problem, (2) the control task for the case that only sensory information are available and no state space representation and (3) a special non-Markov control problem, where the system needs to memorize important events. These three topics address main parts of a robotic control system. In a robotic setting no state space is available, but only sensory information and a control system needs to be able to deal with these (point 2). Furthermore, in real world setting the sensory information alone are not enough. The system needs to identify and memorize important information and actions. For example, a robot that works in a household and uses a camera for navigation will be unable to derive the state of the house'' out of the current image. He needs to remember what he did and what he observed before (point 3). Based on the sensory processing and possibly memorized information the system needs to derive a reasonable control (point 1). I address all three topics in the simplest setting, where the topic makes sense''. I use finite state space Markov Decision Processes (MDPs) for topic 1, a camera based robotic task where the system is Markovian and the sensory information are sufficient for topic 2 and finite state space Partially Observable Markov Decision Processes (POMDPs) for topic 3.
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