Work

Modified Reinforcement Learning Infrastructure

Year: 2014

Type: conference-paper

Abstract: The reinforcement learning (RL) model has been very successful in behavioral sciences, artificial intelligence and neuroscience.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.

Source: Advances in intelligent systems research/Advances in Intelligent Systems Research

DOI: 10.2991/icassr-14.2014.27

Authors: Jyrki Suomala, Ville Suomala

Institutions: Laurea University of Applied Sciences, University of Oulu

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2051454552

FWCI:

Cites: 18

Cited by: 3

Related to: 10