Integrating big data analytics in autonomous driving: An unsupervised hierarchical reinforcement learning approach

Mao, Zhiqi; Liu, Yang; Qu, Xiaobo

doi:10.1016/j.trc.2024.104606

Cited by 1 publication

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, transformer [32] has emerged as a promising tool for training sequential actions, with several studies adopting them for self-driving tasks [33,34]. However, their large network size and computational demands pose significant challenges, particularly in real-time control applications such as autonomous vehicles.…”

Section: Related Workmentioning

confidence: 99%

A Reinforcement Learning Approach to Dynamic Trajectory Optimization with Consideration of Imbalanced Sub-Goals in Self-Driving Vehicles

Kim,

Ahn,

Jang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%