Meta-gradient updates for training return functions for reinforcement learning systems

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reinforcement learning. The embodiments described herein apply meta-learning (and in particular, meta-gradient reinforcement learning) to learn an optimum return function G so that the training of the system is improved. This provides a more effective and efficient means of training a reinforcement learning system as the system is able to converge on an optimum set of one or more policy parameters θ more quickly by training the return function G as it goes. In particular, the return function G is made dependent on the one or more policy parameters θ and a meta-objective function J' is used that is differentiated with respect to the one or more return parameters η to improve the training of the return function G.