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Development of Personalized Autonomous Driving Agents Using Imitation Learning
Ji Hye Ok, Wookyoung Kim, Honguk Woo
http://doi.org/10.5626/JOK.2024.51.6.558
The rise of Autonomous Vehicles (AVs) has brought humans and robots together on the same roads. As AVs integrate into the existing road system, it is crucial for them to establish a connection with human drivers and operate in a way that is convenient to humans. Moreover, as the desire for personalized autonomous driving experiences frows, there is a need to meet the demand for ‘personalized’ AVs. This paper examines imitation learning methods that imitate the driving behaviors of rule-based agents. It also proposes a controlled multi-objective imitation learning approach to generate diverse driving policies based on given data. Additionally, the study assesses the derived policies in various scenarios using the Carla simulator.
A Reinforcement Learning-Based Cache Partitioning Scheme for Multi-Core Environments
Donggyu Choi, Jongseok Kim, Honguk Woo, Euiseong Seo
http://doi.org/10.5626/JOK.2021.48.6.618
Most processors currently in use provide the shared last-level cache (LLC). Therefore, when multiple applications compete intensely for the LLC, its hit ratio is adversely impacted by extremely frequent cache line replacement; this may result in a significant degradation of the overall performance. The hardware-based cache partitioning techniques can relieve this issue by isolating the cache space of a core from others. However, it is necessary to use an adaptive and intelligent cache partitioning algorithm to dynamically determine the optimal cache partition. Reinforcement learning is an appropriate approach for this kind of problems. However, its model complexity skyrockets as the number of the applications to partition increases. This paper proposes a reinforcement learning-based cache partitioning scheme that can support a large number of running applications. Firstly, we built a reinforcement learning model and made it learn to perform cache partitioning for a small number of applications. We then extended it by clustering applications with the information obtained via supervised learning models for cache-use characteristic predictions, which enabled cache partitioning for more applications and resulted in performance gains of up to 19.75%.
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