General value functions (GVFs) in the reinforcement learning (RL) literature are long-term predictive summaries of the outcomes of agents following specific policies in the environment. Affordances as perceived action possibilities with specific valence may be cast into predicted policy-relative goodness and modeled as GVFs. A systematic explication of this connection shows that GVFs and especially their deep-learning embodiments (1) realize affordance prediction as a form of direct perception, (2) illuminate the fundamental connection between action and perception in affordance, and (3) offer a scalable way to learn affordances using RL methods. Through an extensive review of existing literature on GVF applications and representative affordance research in robotics, we demonstrate that GVFs provide the right framework for learning affordances in real-world applications. In addition, we highlight a few new avenues of research opened up by the perspective of “affordance as GVF,” including using GVFs for orchestrating complex behaviors.

强化学习（RL）文献中的一般价值函数（GVFs）是遵循环境中特定政策的行为者结果的长期预测性摘要。可以将满足感作为具有特定效价的感知行动可能性，可以转换为预测的相对政策优势，并建模为GVF。对这种联系的系统说明表明，GVF，尤其是其深度学习实施方案（1）以直接感知的形式实现支付预测，（2）阐明支付行为和感知之间的基本联系，并且（3）提供可扩展性使用RL方法学习能力的方法。通过广泛审查有关GVF应用的现有文献和机器人技术中的代表性收费研究，我们证明了GVF为实际应用中的学习能力提供了正确的框架。此外，我们重点介绍了“以GVF的负担”视角开辟的一些新研究途径，包括使用GVF协调复杂行为。