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Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control
In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity i...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8176548/ https://www.ncbi.nlm.nih.gov/pubmed/34141896 http://dx.doi.org/10.7717/peerj-cs.575 |
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author | Alegre, Lucas N. Bazzan, Ana L.C. da Silva, Bruno C. |
author_facet | Alegre, Lucas N. Bazzan, Ana L.C. da Silva, Bruno C. |
author_sort | Alegre, Lucas N. |
collection | PubMed |
description | In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent. More precisely, we study both the effects of changing the context in which an agent learns (e.g., a change in flow rates experienced by it), as well as the effects of reducing agent observability of the true environment state. Partial observability may cause distinct states (in which distinct actions are optimal) to be seen as the same by the traffic signal agents. This, in turn, may lead to sub-optimal performance. We show that the lack of suitable sensors to provide a representative observation of the real state seems to affect the performance more drastically than the changes to the underlying traffic patterns. |
format | Online Article Text |
id | pubmed-8176548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81765482021-06-16 Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control Alegre, Lucas N. Bazzan, Ana L.C. da Silva, Bruno C. PeerJ Comput Sci Agents and Multi-Agent Systems In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent. More precisely, we study both the effects of changing the context in which an agent learns (e.g., a change in flow rates experienced by it), as well as the effects of reducing agent observability of the true environment state. Partial observability may cause distinct states (in which distinct actions are optimal) to be seen as the same by the traffic signal agents. This, in turn, may lead to sub-optimal performance. We show that the lack of suitable sensors to provide a representative observation of the real state seems to affect the performance more drastically than the changes to the underlying traffic patterns. PeerJ Inc. 2021-05-27 /pmc/articles/PMC8176548/ /pubmed/34141896 http://dx.doi.org/10.7717/peerj-cs.575 Text en © 2021 Alegre et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Agents and Multi-Agent Systems Alegre, Lucas N. Bazzan, Ana L.C. da Silva, Bruno C. Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control |
title | Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control |
title_full | Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control |
title_fullStr | Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control |
title_full_unstemmed | Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control |
title_short | Quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control |
title_sort | quantifying the impact of non-stationarity in reinforcement learning-based traffic signal control |
topic | Agents and Multi-Agent Systems |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8176548/ https://www.ncbi.nlm.nih.gov/pubmed/34141896 http://dx.doi.org/10.7717/peerj-cs.575 |
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