Mark Humphrys - Research - PhD

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Action Selection methods using
Reinforcement Learning

Mark Humphrys

Trinity Hall

A dissertation submitted for the degree of Doctor of Philosophy
in the University of Cambridge

This is the expanded version of my PhD, produced as Technical Report No. 426 in June 1997.
This document can be printed as a single PostScript file here.


The Action Selection problem is the problem of run-time choice between conflicting and heterogenous goals, a central problem in the simulation of whole creatures (as opposed to the solution of isolated uninterrupted tasks). This thesis argues that Reinforcement Learning has been overlooked in the solution of the Action Selection problem. Considering a decentralised model of mind, with internal tension and competition between selfish behaviors, this thesis introduces an algorithm called "W-learning", whereby different parts of the mind modify their behavior based on whether or not they are succeeding in getting the body to execute their actions. This thesis sets W-learning in context among the different ways of exploiting Reinforcement Learning numbers for the purposes of Action Selection. It is a `Minimize the Worst Unhappiness' strategy. The different methods are tested and their strengths and weaknesses analysed in an artificial world.


Chapter 1

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