Version 1
: Received: 15 May 2024 / Approved: 16 May 2024 / Online: 16 May 2024 (09:58:18 CEST)
How to cite:
Kim, Y.-J.; Ahn, W.-J.; Jang, S.-H.; Lim, M.-T.; Pae, D.-S. A Reinforcement Learning Approach to Dynamic Trajectory Optimization with Consideration of Imbalanced Sub-Goals in Self-Driving Vehicles. Preprints2024, 2024051085. https://doi.org/10.20944/preprints202405.1085.v1
Kim, Y.-J.; Ahn, W.-J.; Jang, S.-H.; Lim, M.-T.; Pae, D.-S. A Reinforcement Learning Approach to Dynamic Trajectory Optimization with Consideration of Imbalanced Sub-Goals in Self-Driving Vehicles. Preprints 2024, 2024051085. https://doi.org/10.20944/preprints202405.1085.v1
Kim, Y.-J.; Ahn, W.-J.; Jang, S.-H.; Lim, M.-T.; Pae, D.-S. A Reinforcement Learning Approach to Dynamic Trajectory Optimization with Consideration of Imbalanced Sub-Goals in Self-Driving Vehicles. Preprints2024, 2024051085. https://doi.org/10.20944/preprints202405.1085.v1
APA Style
Kim, Y. J., Ahn, W. J., Jang, S. H., Lim, M. T., & Pae, D. S. (2024). A Reinforcement Learning Approach to Dynamic Trajectory Optimization with Consideration of Imbalanced Sub-Goals in Self-Driving Vehicles. Preprints. https://doi.org/10.20944/preprints202405.1085.v1
Chicago/Turabian Style
Kim, Y., Myo-Taeg Lim and Dong-Sung Pae. 2024 "A Reinforcement Learning Approach to Dynamic Trajectory Optimization with Consideration of Imbalanced Sub-Goals in Self-Driving Vehicles" Preprints. https://doi.org/10.20944/preprints202405.1085.v1
Abstract
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 (MPC) 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
