Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning

• URL: http://arxiv.org/abs/2501.14992v1
• Date: Sat, 25 Jan 2025 00:00:11 GMT
• Title: Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning
• Authors: Zhihao Zhang, Ekim Yurtsever, Keith A. Redmill,
• Abstract summary: Our research introduces a pioneering hierarchical framework that efficiently decomposes intricate decision-making problems into manageable subtasks. We adopt a two step training process that trains the high-level controller and low-level controller separately. The high-level controller exhibits an enhanced exploration potential with long-term delayed rewards, and the low-level controller provides longitudinal and lateral control ability using short-term instantaneous rewards.
• Score: 7.380119332658803
• License:
• Abstract: Developing an automated driving system capable of navigating complex traffic environments remains a formidable challenge. Unlike rule-based or supervised learning-based methods, Deep Reinforcement Learning (DRL) based controllers eliminate the need for domain-specific knowledge and datasets, thus providing adaptability to various scenarios. Nonetheless, a common limitation of existing studies on DRL-based controllers is their focus on driving scenarios with simple traffic patterns, which hinders their capability to effectively handle complex driving environments with delayed, long-term rewards, thus compromising the generalizability of their findings. In response to these limitations, our research introduces a pioneering hierarchical framework that efficiently decomposes intricate decision-making problems into manageable and interpretable subtasks. We adopt a two step training process that trains the high-level controller and low-level controller separately. The high-level controller exhibits an enhanced exploration potential with long-term delayed rewards, and the low-level controller provides longitudinal and lateral control ability using short-term instantaneous rewards. Through simulation experiments, we demonstrate the superiority of our hierarchical controller in managing complex highway driving situations.

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