In this work, we propose a framework of learning with preferences, which combines some neurophysiological findings, prospect theory, and the classic reinforcement learning mechanism. Specifically, we extend the state representation of reinforcement learning with a multi-dimensional preference model controlled by an external state. This external state is designed to be independent from the reinforcement learning process so that it can be controlled by an external process simulating the knowledge and experience of an agent while preserving all major properties of reinforcement learning. Finally, numerical experiments show that our proposed method is capable to learn different preferences in a manner sensitive to the agent’s level of experience.     «

In this work, we propose a framework of learning with preferences, which combines some neurophysiological findings, prospect theory, and the classic reinforcement learning mechanism. Specifically, we extend the state representation of reinforcement learning with a multi-dimensional preference model controlled by an external state. This external state is designed to be independent from the reinforcement learning process so that it can be controlled by an external process simulating the knowledg...     »