Explore-exploit decisions require us to trade off the benefits of exploring unknown options to learn more about them, with exploiting known options, for immediate reward. Such decisions are ubiquitous in nature, but from a computational perspective, they are notoriously hard. There is therefore much interest in how humans and animals make these decisions and recently there has been an explosion of research in this area. Here we provide a biased and incomplete snapshot of this field focusing on the major finding that many organisms use two distinct strategies to solve the explore-exploit dilemma: a bias for information (‘directed exploration’) and the randomization of choice (‘random exploration’). We review evidence for the existence of these strategies, their computational properties, their neural implementations, as well as how directed and random exploration vary over the lifespan. We conclude by highlighting open questions in this field that are ripe to both explore and exploit.

探索-利用决策要求我们权衡探索未知选项以了解更多信息的好处，与利用已知选项以获得即时奖励。这样的决策在自然界中无处不在，但从计算的角度来看，它们是出了名的困难。因此，人们对人类和动物如何做出这些决定非常感兴趣，最近这一领域的研究激增。在这里，我们提供了该领域有偏见且不完整的快照，重点关注许多生物体使用两种不同策略来解决探索-利用困境的主要发现：信息偏见（“定向探索”）和选择的随机化（“随机探索”） '）。我们回顾了这些策略存在的证据、它们的计算特性、它们的神经实现，以及定向和随机探索在生命周期中如何变化。最后，我们强调了该领域中值得探索和利用的悬而未决的问题。