mlsanityData to 7 Oct 2026

Representation Learning for Context-Dependent Decision-Making

arXiv 2205.05820 ↗First posted 12 May 2022cs.LG cs.SY eess.SYcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 12 May 2022.

Builds on

16 of 29 references.

References · 16

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4 more
  1. 2020A Dynamic Observation Strategy for Multi-agent Multi-armed Bandit Problemcited by 10
  2. 2015Satisficing in multi-armed bandit problemscited by 9
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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Stochastic Contextual Bandits with Long Horizon Rewardscited by 1