In this article we hypothesise that intelligence, and its associated abilities, can be understood as subserving the maximisation of reward. Accordingly, reward is enough to drive behaviour that exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalisation and imitation. This is in contrast to the view that specialised problem formulations are needed for each ability, based on other signals or objectives. Furthermore, we suggest that agents that learn through trial and error experience to maximise reward could learn behaviour that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence.

在本文中，我们假设智力及其相关能力可以理解为促进奖励最大化。因此，奖励足以驱动表现出自然和人工智能研究能力的行为，包括知识、学习、感知、社交智能、语言、概括和模仿。这与基于其他信号或目标的每种能力都需要专门的问题表述的观点相反。此外，我们建议通过试错经验学习以最大化奖励的代理可以学习表现出大多数（如果不是全部）这些能力的行为，因此强大的强化学习代理可以构成通用人工智能的解决方案。