Kim, Yu-Jin Ahn, Woo-Jin Jang, Sun-Ho Lim, Myo-Taeg Pae, Dong-Sung 2024-07-11T06:01:23Z 2024-07-11T06:01:23Z 2024-07-11 2024-06 https://pubs.kist.re.kr/handle/201004/150216 Goal-conditioned Reinforcement Learning (RL) holds promise for addressing intricate control challenges by enabling agents to learn and execute desired skills through separate decision modules. However, the irregular occurrence of required skills poses a significant challenge to effective learning. In this paper, we demonstrate the detrimental effects of this imbalanced skill (sub-goal) distribution and propose a novel training approach, Classified Experience Replay (CER), designed to mitigate this challenge. We demonstrate that adapting our method to conventional RL methods significantly enhances the performance of the RL agent. Considering the challenges inherent in tasks such as driving, characterized by biased occurrences of required sub-goals, our study demonstrates the improvement in trained outcomes facilitated by the proposed method. In addition, we introduce a specialized framework tailored for self-driving tasks on highways, integrating model predictive control into our RL trajectory optimization training paradigm. Our approach, utilizing CER with the suggested framework, yields remarkable advancements in trajectory optimization for RL agents operating in highway environments. English MDPI A Reinforcement Learning Approach to Dynamic Trajectory Optimization with Consideration of Imbalanced Sub-Goals in Self-Driving Vehicles Article 10.3390/app14125213 1 Applied Sciences-basel, v.14, no.12 Applied Sciences-basel 14 12 Y scie 001254443800001 2-s2.0-85197244022 Chemistry, Multidisciplinary Engineering, Multidisciplinary Materials Science, Multidisciplinary Physics, Applied Chemistry Engineering Materials Science Physics Article reinforcement learning experience replay self-driving trajectory optimization