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Reinforcement learning using continuous states and interactive feedback

conference contribution
posted on 01.01.2019, 00:00 authored by A Ayala, C Henríquez, F Cruz Naranjo
Research in intelligent systems field has led to different learning methods for machines to acquire knowledge, among them, reinforcement learning (RL). Given the problem of the time required to learn how to develop a problem, using RL this work tackles the interactive reinforcement learning (IRL) approach as a way of solution for the training of agents. Furthermore, this work also addresses the problem of continuous representations along with the interactive approach. In this regards, we have performed experiments with simulated environments using different representations in the state vector in order to show the efficiency of this approach under a certain probability of interaction. The obtained results in the simulated environments show a faster learning convergence when using continuous states and interactive feedback in comparison to discrete and autonomous reinforcement learning respectively.

History

Event

Applications of Intelligent Systems. Conference (2nd : 2019 : Las Palmas de Gran Canaria, Spain)

Series

Applications of Intelligent Systems Conference

Pagination

1 - 5

Publisher

Association for Computing Machinery

Location

Las Palmas de Gran Canaria, Spain

Place of publication

New York, N.Y.

Start date

07/01/2019

End date

09/01/2019

ISBN-13

9781450360852

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2019, the owner/author(s)

Editor/Contributor(s)

N Petkov, N Strisciuglio, C Travieso

Title of proceedings

APPIS 19 : Proceedings of the 2nd International Conference on Applications of Intelligent Systems