Exploring DQN-Based Reinforcement Learning in Autonomous Highway Navigation Performance Under High-Traffic Conditions

Nugroho ; Sandy ; Setiadi ; De Rosal Ignatius Moses ; Islam ; Hussain Md Mehedul

Journal of Computing Theories and Applications 📅 2024 Vol 1, No 3 🔗 DOI: 10.62411/jcta.9929

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%.

Keywords

Autonomous Highway Navigation; Autonomous Vehicle Navigation; Crowded Traffic Autonomous; Deep-Q Networking; Reinforcement Learning

How to Cite

Nugroho, et al. (2024). Exploring DQN-Based Reinforcement Learning in Autonomous Highway Navigation Performance Under High-Traffic Conditions. Journal of Computing Theories and Applications, 1(3). https://doi.org/10.62411/jcta.9929

Nugroho, Sandy; Setiadi, De Rosal Ignatius Moses; Islam, Hussain Md Mehedul, "Exploring DQN-Based Reinforcement Learning in Autonomous Highway Navigation Performance Under High-Traffic Conditions," Journal of Computing Theories and Applications, vol. 1, no. 3, 2024.

Nugroho, Sandy; Setiadi, De Rosal Ignatius Moses; Islam, Hussain Md Mehedul. "Exploring DQN-Based Reinforcement Learning in Autonomous Highway Navigation Performance Under High-Traffic Conditions." Journal of Computing Theories and Applications, vol. 1, no. 3, 2024.

Nugroho, et al. (2024) 'Exploring DQN-Based Reinforcement Learning in Autonomous Highway Navigation Performance Under High-Traffic Conditions', Journal of Computing Theories and Applications, 1(3). doi: 10.62411/jcta.9929.

Nugroho, Sandy; Setiadi, De Rosal Ignatius Moses; Islam, Hussain Md Mehedul. Exploring DQN-Based Reinforcement Learning in Autonomous Highway Navigation Performance Under High-Traffic Conditions. Journal of Computing Theories and Applications. 2024;1(3).

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