Cognitive Reinforcement Learning: An Interpretable Decision-Making for Virtual Driver

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE journal of radio frequency identification Pub Date : 2024-06-24 DOI:10.1109/JRFID.2024.3418649
Hao Qi;Enguang Hou;Peijun Ye
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Abstract

The interpretability of decision-making in autonomous driving is crucial for the building of virtual driver, promoting the trust worth of artificial intelligence (AI) and the efficiency of human-machine interaction. However, current data-driven methods such as deep reinforcement learning (DRL) directly acquire driving policies from collected data, where the decision-making process is vague for safety validation. To address this issue, this paper proposes cognitive reinforcement learning that can both simulate the human driver’s deliberation and provide interpretability of the virtual driver’s behaviors. The new method involves cognitive modeling, reinforcement learning and reasoning path extraction. Experiments on the virtual driving environment indicate that our method can semantically interpret the virtual driver’s behaviors. The results show that the proposed cognitive reinforcement learning model combines the interpretability of cognitive models with the learning capability of reinforcement learning, providing a new approach for the construction of trustworthy virtual drivers.
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认知强化学习:虚拟驾驶员的可解释决策
自动驾驶中决策的可解释性对于构建虚拟驾驶员、提升人工智能(AI)的信任价值和人机交互效率至关重要。然而,目前的数据驱动方法(如深度强化学习(DRL))直接从收集的数据中获取驾驶策略,决策过程在安全验证方面比较模糊。针对这一问题,本文提出了认知强化学习方法,既能模拟人类驾驶员的思考过程,又能提供虚拟驾驶员行为的可解释性。新方法包括认知建模、强化学习和推理路径提取。虚拟驾驶环境的实验表明,我们的方法可以从语义上解释虚拟驾驶员的行为。结果表明,所提出的认知强化学习模型结合了认知模型的可解释性和强化学习的学习能力,为构建可信赖的虚拟驾驶员提供了一种新方法。
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