Modified Reinforcement Learning Infrastructure

DOI

10.2991/icassr-14.2014.27How to use a DOI?

Keywords

MRLI, Graph Theory, Learning, Behavioural model, Decision-making.

Abstract

The reinforcement learning (RL) model has been very successful in behavioural sciences, artificial intelligence and neuro- science. Despite its fruitfulness in many simple situations, the RL model does not always cope well with real life situations involving a large space of possible world states or a large set of possible actions. We propose a modified version of the RL learning model. The benefit of this model is that the temporal difference prediction error can be used directly to update not only the value of the latest action of the learning agent, but the values of many possible future actions. An example application of this modified reinforcement learning infrastructure (MRLI) is presented for a customer behaviour in a complex shopping environment.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jyrki Suomala
AU  - Ville Suomala
PY  - 2014/07
DA  - 2014/07
TI  - Modified Reinforcement Learning Infrastructure
BT  - Proceedings of the 2nd International Conference on Applied Social Science Research
PB  - Atlantis Press
SP  - 95
EP  - 97
SN  - 1951-6851
UR  - https://doi.org/10.2991/icassr-14.2014.27
DO  - 10.2991/icassr-14.2014.27
ID  - Suomala2014/07
ER  -