The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning.

ABSTRACT: While there is no doubt that social signals affect human reinforcement learning, there is still no consensus about how this process is computationally implemented. To address this issue, we compared three psychologically plausible hypotheses about the algorithmic implementation of imitation in reinforcement learning. The first hypothesis, decision biasing (DB), postulates that imitation consists in transiently biasing the learner's action selection without affecting their value function. According to the second hypothesis, model-based imitation (MB), the learner infers the demonstrator's value function through inverse reinforcement learning and uses it to bias action selection. Finally, according to the third hypothesis, value shaping (VS), the demonstrator's actions directly affect the le

SUBMITTER: Najar A 

PROVIDER: S-EPMC7723279 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature