Self-Consistent Models and Values

Understand

Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment.

• In particular, models enable planning, i.e.
• using more computation to improve value functions or policies, without requiring additional environment interactions.
• In this work, we investigate a way of augmenting model-based RL, by additionally encouraging a learned model and value function to be jointly \emph{self-consistent}.