Gujgiczer, Dániel Tamás and Szabó, Ádám and Bécsi, Tamás (2023) Comparison of Single- and Multi-Agent Reinforcement Learning for Highway Driving. In: 2nd IEEE International Conference on Cognitive Mobility 2023, 2023.10.19. - 2023.10.20., Budapest (Hungary). (In Press)
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Abstract
Single-agent reinforcement learning has been widely used to solve several traffic-related problems. These algorithms show promising results in their training environments, but their performance remains questioned when they meet other agents with the same strategy. This paper aims to show the advantages of multi-agent reinforcement learning in autonomous driving using a simple multilane highway scenario as an example. The performance of single- and multi-agent learning policies have been compared in mixed traffic. Furthermore, the effects of additional information in the observation space have also been analyzed.
| Item Type: | Conference or Workshop Item (Paper) |
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| Subjects: | T Technology / alkalmazott, műszaki tudományok > T2 Technology (General) / műszaki tudományok általában T Technology / alkalmazott, műszaki tudományok > TE Highway engineering. Roads and pavements / közlekedésmérnöki, útépítési technika |
| Depositing User: | Dr. Tamás Bécsi |
| Date Deposited: | 26 Sep 2023 12:32 |
| Last Modified: | 26 Sep 2023 12:32 |
| URI: | http://real.mtak.hu/id/eprint/175046 |
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