The integration of autonomous vehicles into modern transportation systems demands sophisticated adaptive control strategies to ensure safety, efficiency, and reliability. This paper explores the application of reinforcement learning (RL) in developing such strategies for autonomous vehicles. We delve into how RL algorithms can be employed to optimize decision-making processes in dynamic environments, where real-time adjustments are crucial. The focus is on the challenges faced by autonomous systems when dealing with uncertainty and unpredictability on the road. By leveraging the strengths of RL, including exploration-exploitation trade-offs, reward-based learning, and adaptation to changing conditions, this study presents a framework for enhancing the control capabilities of autonomous vehicles. The effectiveness of this approach is demonstrated through simulations and real-world experiments, highlighting the potential of RL in shaping the future of autonomous driving.
Martin, J. Adaptive Control Strategies in Autonomous Vehicles Using Reinforcement Learning. Transactions on Applied Soft Computing, 2019, 1, 2. https://doi.org/10.69610/j.tasc.20190930
AMA Style
Martin J. Adaptive Control Strategies in Autonomous Vehicles Using Reinforcement Learning. Transactions on Applied Soft Computing; 2019, 1(1):2. https://doi.org/10.69610/j.tasc.20190930
Chicago/Turabian Style
Martin, James 2019. "Adaptive Control Strategies in Autonomous Vehicles Using Reinforcement Learning" Transactions on Applied Soft Computing 1, no.1:2. https://doi.org/10.69610/j.tasc.20190930
APA style
Martin, J. (2019). Adaptive Control Strategies in Autonomous Vehicles Using Reinforcement Learning. Transactions on Applied Soft Computing, 1(1), 2. https://doi.org/10.69610/j.tasc.20190930
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