Modified Reinforcement Learning Infrastructure
Jyrki Suomala, Ville Suomala
Available Online July 2014.
- https://doi.org/10.2991/icassr-14.2014.27How to use a DOI?
- MRLI, Graph Theory, Learning, Behavioural model, Decision-making.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/icassr-14.2014.27 ID - Suomala2014/07 ER -