论文标题
代表性学习与上下文有关的决策
Representation Learning for Context-Dependent Decision-Making
论文作者
论文摘要
人类能够灵活，快速地适应不断变化的环境。经验证据表明，表示学习在赋予人类具有这样的能力方面起着至关重要的作用。受到这一观察的启发，我们研究了在顺序决策场景中的表示形式学习，并通过上下文变化。我们提出了一种在线算法，该算法能够学习和转移与上下文相关的表示形式，并表明它的表现明显优于未经适应性的现有表示。作为一个案例研究，我们将算法应用于威斯康星州卡分类任务，这是对人类在顺序决策中的心理灵活性的良好测试。通过将我们的算法与标准Q学习和深Q学习算法进行比较，我们证明了自适应表示学习的好处。
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.
