Schemes for learning and behaviour: A new expectancy model (1997)
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BibTeX
@TECHREPORT{Witkowski97schemesfor,
author = {Christopher Mark Witkowski},
title = {Schemes for learning and behaviour: A new expectancy model},
institution = {},
year = {1997}
}
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Abstract
This thesis presents a novel form of learning by reinforcement. Existing reinforcement learning algorithms rely on the provision of external reward signals to drive the learning algorithm. This new algorithm relies on reinforcing signals generated internally within the algorithm. The algorithm, SRS/E, described here generates expectancies (-hypotheses), each of which gives rise to a specific prediction when the conditions relevant to the expectancy are encountered (the-experiment). The algorithm subsequently tests these predictions against actual events and so generates reinforcement signals to corroborate or reject individual expectancies. This procedure allows for self-contained, completely unsupervised learning to an extent not possible with previous reinforcement procedures. The SRS/E algorithm is derived from a number of postulates that constitute a new Dynamic Expectancy Model developed in this thesis. In contrast to the static policy map generated by existing Q-learning based reinforcement algorithms, which limit learning to one goal, the SRS/E algorithm generates a Dynamic Policy Map (DPM) from learned expectancies whenever a
