论文标题
在适应性的国际象棋环境中学习的强化学习，用于检测人类理解的概念
Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts
论文作者
论文摘要
使用机器学习开发的自主代理在各种控制环境中都表现出巨大的希望，这在涉及自动驾驶汽车的应用中最为明显。与深度神经网络形式的自学代理相关的主要挑战是它们的黑盒本质：人类不可能解释深度神经网络。因此，人类不能直接解释基于神经网络的代理的行为，也不能在不同的情况下预见其鲁棒性。在这项工作中，我们演示了一种探测哪种概念在训练过程中内在化的方法。为了进行演示，我们在一个专门开发的快速和光明环境中使用国际象棋代理，适合研究小组，而无需访问庞大的计算资源或机器学习模型。
Self-trained autonomous agents developed using machine learning are showing great promise in a variety of control settings, perhaps most remarkably in applications involving autonomous vehicles. The main challenge associated with self-learned agents in the form of deep neural networks, is their black-box nature: it is impossible for humans to interpret deep neural networks. Therefore, humans cannot directly interpret the actions of deep neural network based agents, or foresee their robustness in different scenarios. In this work, we demonstrate a method for probing which concepts self-learning agents internalise in the course of their training. For demonstration, we use a chess playing agent in a fast and light environment developed specifically to be suitable for research groups without access to enormous computational resources or machine learning models.
