“Learning Relative Return Policies With Upside-Down Reinforcement Learning”, 2022-02-23 (; similar):
Lately, there has been a resurgence of interest in using supervised learning to solve reinforcement learning problems. Recent work in this area has largely focused on learning command-conditioned policies.
We investigate the potential of one such method—upside-down reinforcement learning—to work with commands that specify a desired relationship between some scalar value and the observed return. We show that upside-down reinforcement learning can learn to carry out such commands online in a tabular bandit setting and in CartPole with non-linear function approximation.
By doing so, we demonstrate the power of this family of methods and open the way for their practical use under more complicated command structures.