Humans are capable of adjusting to changing environments flexibly and quickly. Empirical evidence has revealed that representation learning plays a crucial role in endowing humans with such a capability. Inspired by this observation, we study representation learning in the sequential decision-making scenario with contextual changes. We propose an online algorithm that is able to learn and transfer context-dependent representations and show that it significantly outperforms the existing ones that do not learn representations adaptively. As a case study, we apply our algorithm to the Wisconsin Card Sorting Task, a well-established test for the mental flexibility of humans in sequential decision-making. By comparing our algorithm with the standard Q-learning and Deep-Q learning algorithms, we demonstrate the benefits of adaptive representation learning.

翻译：人类能够灵活和迅速地适应不断变化的环境。经验证据表明,代表性学习在赋予人类这种能力方面发挥着关键作用。受此观察的启发,我们研究在顺序决策情景中的代表性学习,同时进行背景变化。我们提出一种在线算法,能够学习和转移基于背景的表示,并表明它大大优于不适应性地学习表现的现有算法。作为案例研究,我们将我们的算法应用到威斯康星牌排序任务中,这是对人类在顺序决策中精神灵活性的既定测试。通过将我们的算法与标准的Q学习和深Q学习算法进行比较,我们展示了适应性代表学习的好处。