By decomposing policies and value functions into frozen basis functions weighted by a shared low-dimensional goal embedding, agents can adapt to novel tasks instantly without retraining, enabling rapid transfer in complex control problems.
This paper presents a method for quickly adapting reinforcement learning agents to new tasks by sharing a low-dimensional goal embedding between policy and value functions.