Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving

📰 ArXiv cs.AI

Hybrid action-based reinforcement learning for autonomous driving achieves multi-objective compatibility

advanced Published 31 Mar 2026

Action Steps

Design a hybrid action-based reinforcement learning framework
Implement a multi-objective compatible policy updating mechanism
Evaluate the framework using diverse driving scenarios and metrics
Fine-tune the model for better performance and compatibility

Who Needs to Know This

AI engineers and researchers working on autonomous driving projects can benefit from this approach to improve decision-making and control in diverse driving scenarios

Key Insight

💡 Hybrid action-based reinforcement learning can effectively address the challenges of multi-objective compatibility in autonomous driving

Key Takeaways

Hybrid action-based reinforcement learning for autonomous driving achieves multi-objective compatibility

Full Article

Title: Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving

Abstract:
arXiv:2501.08096v4 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has shown excellent performance in solving decision-making and control problems of autonomous driving, which is increasingly applied in diverse driving scenarios. However, driving is a multi-attribute problem, leading to challenges in achieving multi-objective compatibility for current RL methods, especially in both policy updating and policy execution. On the one hand, a single value evaluation network limits

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