EPIA2019_paper_249.pdf (378.09 kB)
Spatio-temporal attention deep recurrent Q-network for POMDPs
conference contribution
posted on 2019-11-27, 10:02 authored by Mariano Etchart, Pawel Ladosz, David Mulvaney© Springer Nature Switzerland AG 2019. One of the long-standing challenges for reinforcement learning agents is to deal with noisy environments. Although progress has been made in producing an agent capable of optimizing its environment in fully observable conditions, partial observability still remains a difficult task. In this paper, a novel model is proposed which inspired by human perception, utilizes two fundamental machine learning concepts, attention and memory, to better confront a noisy environment.
History
School
- Mechanical, Electrical and Manufacturing Engineering
- Science
Department
- Computer Science
Published in
Progress in Artificial Intelligence: 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Vila Real, Portugal, September 3–6, 2019, Proceedings.Volume
Part IPages
98 - 105Publisher
SpringerVersion
- AM (Accepted Manuscript)
Publisher statement
This is a pre-copyedited version of a contribution published in Progress in Artificial Intelligence: 19th EPIA Conference on Artificial Intelligence, EPIA 2019. edited Paulo Moura Oliveira • Paulo Novais • Luís Paulo Reis (Eds.) published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-030-30241-2_9Publication date
2019-08-30Copyright date
2019ISBN
9783030302405ISSN
0302-9743eISSN
1611-3349Publisher version
Book series
Lecture Notes in Computer Science vol 11804; Lecture Notes in Artificial Intelligence vol 11804Language
- en
Editor(s)
Paulo Moura Oliveira • Paulo Novais • Luís Paulo ReisLocation
Vila Real, PortugalEvent dates
September 3–6Depositor
Dr David Mulvaney. Deposit date: 26 November 2019Usage metrics
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