Social reinforcement learning with shared reference satisficing

Abstract

animals learn not only through individual trial-and-error, but also from other individuals. It is known that vertebrates cleverly utilize learning strategies such as copy-when-uncertain and copy-successful-individuals. These strategies can be applied to social reinforcement learning, although their formalizations are yet to be established. We propose a social reinforcement learning algorithm with a very narrow information sharing. The algorithm exploits RS value function that models the satisficing principle for exploration and exploitation.