Distributional Reinforcement Learning with Ensembles
This work addresses performance enhancement in reinforcement learning for AI applications, but it is incremental as it builds on existing ensemble and distributional methods.
The paper tackles the problem of improving reinforcement learning performance by integrating ensemble methods into distributional reinforcement learning, resulting in more robust initial learning, stronger individual performance, and good efficiency per sample.
It is well known that ensemble methods often provide enhanced performance in reinforcement learning. In this paper, we explore this concept further by using group-aided training within the distributional reinforcement learning paradigm. Specifically, we propose an extension to categorical reinforcement learning, where distributional learning targets are implicitly based on the total information gathered by an ensemble. We empirically show that this may lead to much more robust initial learning, a stronger individual performance level, and good efficiency on a per-sample basis.