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 Autonomous Driving Strategy for Bottleneck Traffic with Prioritized Experience Replay 


Vol. 48,  No. 6, pp. 690-703, Jun.  2023
10.7840/kics.2023.48.6.690


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  Abstract

Recently, the demand for a higher level of autonomous driving technology in complex circumstances has increased. Deep reinforcement learning is gaining attention as a promising solution that enables instant decision-making based on high-dimensional state information. In this study, we propose a Partially Observable Markov Decision Process (POMDP) to train the autonomous driving policy that can successfully navigate bottleneck congestion. Furthermore, we suggest using the prioritized experience replay method in Twin Delayed Deep Deterministic Policy Gradient (TD3) to train the policy. As a result, we confirm that the vehicles trained with the prioritized experience replay method outperform the vehicles trained with the random experience replay method.

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[IEEE Style]

C. Eom, D. Lee, M. Kwon, "Autonomous Driving Strategy for Bottleneck Traffic with Prioritized Experience Replay," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 6, pp. 690-703, 2023. DOI: 10.7840/kics.2023.48.6.690.

[ACM Style]

Chanin Eom, Dongsu Lee, and Minhae Kwon. 2023. Autonomous Driving Strategy for Bottleneck Traffic with Prioritized Experience Replay. The Journal of Korean Institute of Communications and Information Sciences, 48, 6, (2023), 690-703. DOI: 10.7840/kics.2023.48.6.690.

[KICS Style]

Chanin Eom, Dongsu Lee, Minhae Kwon, "Autonomous Driving Strategy for Bottleneck Traffic with Prioritized Experience Replay," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 6, pp. 690-703, 6. 2023. (https://doi.org/10.7840/kics.2023.48.6.690)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880