奖励大小决定强化学习效率

Full article and list of author affiliations: https://doi.org/10.1126/ science.aeb0813 Sheng Gong, Alyssa Martell, Joshua T. Dudman*, Luke T. Coddington* INTRODUCTION: Across different disciplines that share an interest in learning, from artifical intelligence (AI) to experimental psychology, it has long been assumed that there is a free parameter, the learning rate, that determines individual variance in learning efficiency and is relatively independent of the magnitude of reward. This suggests that learning depends primarily on the amount of experience (number of rewards). However, recent theoretical work mapping dopamine (DA) function onto reinforcement learning algorithms, combined with classic results on DA encoding of reward, suggested that learning rates might in fact depend upon reward magnitude. …