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Learn a Robust Policy for Real-World Driving With Adversarial Reinforcement Learning
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DOI：10.1109/TIV.2026.3667572.png)
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Abstract
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Reinforcement learning (RL) has emerged as a promising approach for achieving high-level autonomous driving as its self-evolving ability without reliance on human rules. Although RL-driven methods have yielded fruitful demonstrations in driving domain, most of them are trained and validated only on simulation platforms. Due to the inherent difference between simulation and reality, the driving policy usually performs poorly when applied to a realistic environment. In this paper, we propose an adversarial training framework for driving policy to enhance practical performance, which introduces the adversarial policy to simulate the discrepancy of environments during training and incorporates the collected data from the real world to provide the discrepancy bound. Besides, the adversarial policy is iteratively updated with gradient-based optimization, which enables the automatic generation of diversified discrepancies for different traffic participants. The action projection is developed to ensure that the output of adversary satisfies the discrepancy bound given by data, so as to prevent an aggressive adversary making the overly conservative policy. We evaluate the trained policy on a fully sized vehicle at an urban intersection with mixed traffic flows. Results indicate that our driving policy can handle unseen behaviors of traffic actors meanwhile realizing the safe and smooth control for the automated vehicle. Our work provides a feasible solution for RL implementation in the field of real-world autonomous driving.
Keywords:
Automated vehicle
reinforcement learning
real-world driving
constraint optimization
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