There is an apparent connection between reinforcement learning and agency. Artificial entities controlled by reinforcement learning algorithms are standardly referred to as agents, and the mainstream view in the psychology and neuroscience of agency is that humans and other animals are reinforcement learners. This article examines this connection, focusing on artificial reinforcement learning systems and assuming that there are various forms of agency. Artificial reinforcement learning systems satisfy plausible conditions for minimal agency, and those which use models of the environment (...) to perform forward search are capable of a form of agency which may reasonably be called action for reasons. (shrink)

—Commentary on Mikhalevich and Powell on invertebrate minds.— Whether or not arthropods are sentient, they can have moral standing. Appeals to sentience are not necessary and retard progress in human treatment of other species, including invertebrates. Other increasingly well-documented aspects of invertebrate minds are pertinent to their welfare. Even if arthropods are not sentient, they can be agents whose goals—and therefore interests—can be frustrated. This kind of agency is sufficient for moral status and requires that we consider their welfare.