The University of Edinburgh -
Division of Informatics
Forrest Hill & 80 South Bridge

Research Paper #784

Title:Issues in Putting Reinforcement Learning Onto Robots
Date:Jan 1996
Presented:Presented at AISB-95 Workshop on Mobile Robots
Abstract:There has recently been a good deal of interest in robot learning. Reinforcement Learning (RL) is a trial and error approach to learning that has recently become popular with roboticists. This is despite the fact that RL methods are very slow, and scale badly with the size of the state and action spaces, thus making them difficult to put onto real robots. This paper describes some work I have been doing on trying to understand why RL methods are so slow and how they might be speeded up. A reinforcement learning algorithm loosely based on the theory of hypothesis testing is presented as are some preliminary results from employing this algorithm on a set of bandit problems.

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