A central goal of artificial intelligence in high-stakes decision-making applications is to design a single algorithm that simultaneously expresses generalizability by learning coherent representations of their world and interpretable explanations of its dynamics. Here, we combine brain-inspired neural computation principles and scalable deep learning architectures to design compact neural controllers for task-specific compartments of a full-stack autonomous vehicle control system. We discover that a single algorithm with 19 control neurons, connecting 32 encapsulated input features to outputs by 253 synapses, learns to map high-dimensional inputs into steering commands. This system shows superior generalizability, interpretability and robustness compared with orders-of-magnitude larger black-box learning systems. The obtained neural agents enable high-fidelity autonomy for task-specific parts of a complex autonomous system.

人工智能在高风险决策应用程序中的主要目标是设计一个单一算法，该算法通过学习其世界的连贯表示形式及其动力学的可解释性解释，来同时表达可概括性。在这里，我们结合了灵感来自大脑的神经计算原理和可扩展的深度学习体系结构，为全栈自动驾驶汽车控制系统的任务专用隔间设计紧凑型神经控制器。我们发现，具有19个控制神经元的单个算法通过253个突触将32个封装的输入特征连接到输出，可以学习将高维输入映射到操纵命令。与数量级较大的黑匣子学习系统相比，该系统显示出卓越的通用性，可解释性和鲁棒性。