Nugroho, Sandy and Setiadi, De Rosal Ignatius Moses and Islam, Hussain Md Mehedul (2024) Exploring DQN-Based Reinforcement Learning in Autonomous Highway Navigation Performance Under High-Traffic Conditions. Journal of Computing Theories and Applications, 1 (3). pp. 274-286. ISSN 3024-9104
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Abstract
Driving in a straight line is one of the fundamental tasks for autonomous vehicles, but it can become complex and challenging, especially when dealing with high-speed highways and dense traffic conditions. This research aims to explore the Deep-Q Networking (DQN) model, which is one of the reinforcement learning (RL) methods, in a highway environment. DQN was chosen due to its proficiency in handling complex data through integrated neural network approximations, making it capable of addressing high-complexity environments. DQN simulations were conducted across four scenarios, allowing the agent to operate at speeds ranging from 60 to nearly 100 km/h. The simulations featured a variable number of vehicles/obstacles, ranging from 20 to 80, and each simulation had a duration of 40 seconds within the Highway-Env simulator. Based on the test results, the DQN method exhibited excellent performance, achieving the highest reward value in the first scenario, 35.6117 out of a maximum of 40, and a success rate of 90.075%.
Item Type: | Article |
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Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Depositing User: | dl fts |
Date Deposited: | 29 Nov 2024 00:57 |
Last Modified: | 29 Nov 2024 01:26 |
URI: | https://dl.futuretechsci.org/id/eprint/45 |