Learning Relative Return Policies With Upside-Down Reinforcement Learning

Understand

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.