The recent paper `"Reward is Enough" by Silver, Singh, Precup and Sutton posits that the concept of reward maximisation is sufficient to underpin all intelligence, both natural and artificial. We contest the underlying assumption of Silver et al. that such reward can be scalar-valued. In this paper we explain why scalar rewards are insufficient to account for some aspects of both biological and computational intelligence, and argue in favour of explicitly multi-objective models of reward maximisation. Furthermore, we contend that even if scalar reward functions can trigger intelligent behaviour in specific cases, it is still undesirable to use this approach for the development of artificial general intelligence due to unacceptable risks of unsafe or unethical behaviour.

该论文提出了奖励最大化是所有智能的基础，但我们认为标量奖励无法解释生物和计算智能的某些方面，因此应采用显式的多目标奖励模型，并且即使标量奖励可以触发智能行为，也应避免使用这种方法来开发人工通用智能，因为会存在不安全或不道德的行为风险。

标量奖励不足够：对Silver、Singh、Precup和Sutton（2021）的回应