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Reinforcement learning using continuous states and interactive feedback
conference contribution
posted on 2019-01-01, 00:00 authored by A Ayala, C Henríquez, F Cruz NaranjoResearch 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.
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Event
Applications of Intelligent Systems. Conference (2nd : 2019 : Las Palmas de Gran Canaria, Spain)Series
Applications of Intelligent Systems ConferencePagination
1 - 5Publisher
Association for Computing MachineryLocation
Las Palmas de Gran Canaria, SpainPlace of publication
New York, N.Y.Publisher DOI
Start date
2019-01-07End date
2019-01-09ISBN-13
9781450360852Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2019, the owner/author(s)Editor/Contributor(s)
N Petkov, N Strisciuglio, C TraviesoTitle of proceedings
APPIS 19 : Proceedings of the 2nd International Conference on Applications of Intelligent SystemsUsage metrics
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