종방향 주행성능향상을 위한 Latent SAC 강화학습 보상함수 설계
On the Reward Function of Latent SAC Reinforcement Learning to Improve Longitudinal Driving Performance
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초록
최근 심층강화학습을 활용한 종단간 자율주행에 대한 관심이 크게 증가하고 있다. 본 논문에서는 차량의 종방향 주행 성능을 개선하는 잠재 SAC 기반 심층강화학습의 보상함수를 제시한다. 기존 강화학습 보상함수는 주행 안전성과 효율성이 크게저하되는 반면 제시하는 보상함수는 전방 차량과의 충돌위험을 회피하면서 적절한 차간거리를 유지할 수 있음을 보인다.
Abstract
In recent years, there has been a strong interest in the end-to-end autonomous driving based on deep reinforcementlearning. In this paper, we present a reward function of latent SAC deep reinforcement learning to improve the longitudinaldriving performance of an agent vehicle. While the existing reward function significantly degrades the driving safety andefficiency, the proposed reward function is shown to maintain an appropriate headway distance while avoiding the frontvehicle collision.
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