Shanmugasundaram, P and Bhatnagar, S (2022) Co-operative Multi-agent Twin Delayed DDPG for Robust Phase Duration Optimization of Large Road Networks. In: 14th International Conference on Agents and Artificial Intelligence, ICAART 2022, 3 - 5 February 2022, Virtual, Online, pp. 122-142.
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Large road networks overflowing with vehicles have called for increased traffic congestion, the impact of which is felt on an everyday basis and across different dimensions like decreased traveller satisfaction, increased fuel usage and increased air pollution among many other troubles. Improved traffic control strategies that can self-learn to adapt their decisioning in response to dynamic changes in the traffic flows and are capable of mitigating overall network congestion as opposed to localized congestion at intersections, are of great importance in mitigating traffic congestion. Traffic control strategies which were rule-based or historical-demand based were over-simplified and could not scale to large real-world road networks. To effectively control traffic congestion at scale, the need for co-operation and communication between the different intersections of a large road network is crucial. Multi-agent reinforcement learning methods are an apt choice for traffic signal control of large scale road networks as they can learn to perform predictive control actions that will reduce overall network congestion dynamically at scale. In this paper, we extend the work done in 24 to traffic signal timing (green phase duration) control using Multi-agent Twin Delayed Deep Deterministic Policy Gradients (MATD3) on large scale real-world road networks. The solution strategy was exposed to simulations of different road networks and time-varying traffic flows. The experimental results showed that our strategy is robust to the different kinds of road networks and vehicular traffic flows, and consistently outperformed its adaptive and rule-based counterparts by significantly reducing the average vehicular delay and queue length. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH. |
| Keywords: | Deep learning; Motor transportation; Multi agent systems; Pollution control; Roads and streets; Street traffic control; Timing circuits; Traffic congestion; Traffic signals, Adaptive congestion control; Control strategies; Deep reinforcement learning; Multi agent; Multi-agent reinforcement learning; Phase duration; Reinforcement learnings; Road network; Traffic flow; Traffic signal timing control, Reinforcement learning |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 27 Mar 2023 11:16 |
| Last Modified: | 27 Mar 2023 11:16 |
| URI: | https://eprints.iisc.ac.in/id/eprint/81203 |
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