Imagination-augmented reinforcement learning framework for variable speed limit control

Li, D ORCID: https://orcid.org/0000-0003-0142-9290 and Lasenby, J, 2023. Imagination-augmented reinforcement learning framework for variable speed limit control. IEEE Transactions on Intelligent Transportation Systems. ISSN 1524-9050

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Abstract

Variable Speed Limit (VSL) is a commonly applied active traffic management measure for urban motorways. In recent years, model-based and model-free approaches have been extensively adopted to solve VSL optimization problems. However, the success of model-based VSL relies heavily on the nature of the environmental model adopted (e.g., traffic flow model). Implicit environment models may result in inappropriate control actions. Although model-free approaches are able to directly map raw measurements to control actions without a need for an environment model, they usually require large amounts of training data. In order to address these issues, we propose an Imagination-Augmented Agent (I2A) for VSL control. The I2A consists an imagination path and a model-free path, which work together to generate appropriate control actions. The simulation results show that the proposed I2A agent outperforms other tested Reinforcement Learning (RL) agents in terms of Total Time Spent and bottleneck volume.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Intelligent Transportation Systems
Creators:	Li, D. and Lasenby, J.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	27 September 2023
ISSN:	1524-9050
Identifiers:	Number Type 10.1109/tits.2023.3316285 DOI 1825012 Other
Rights:	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	20 Dec 2023 15:23
Last Modified:	20 Dec 2023 15:23
URI:	https://irep.ntu.ac.uk/id/eprint/50583

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