Chapter . Application of reinforcement learning to a mobile robot in reaching recharging station operation
L. Cragg and H. Hu
Department of Computer Science, University of Essex, Colchester C04 3SQ, U.K. Email: lmcrag@essex.ac.uk, hhu@essex.ac.uk
Abstract
Efficient control strategies for robot systems cannot always be developed by hand, especially when the robot system is operating in an unknown or uncertain environment. In this paper we show how Reinforcement Learning (RL) might be applied to improve the efficiency of a mobile robot in nuclear decommissioning characterisation, in particular allowing it to learn efficient routes back to a recharging station. We implement this learning functionality in a mobile agent (MA) environment. By doing so ...
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