Zero-shot stitching in Reinforcement Learning using Relative Representations

Abstract

In this paper we investigate the use of a recent method called “relative represen- tations” to enable zero-shot model stitching in visual RL between encoders and policies trained on the CarRacing environment, which does not require additional training. Our experiments show that the relative representation framework can be effectively applied to the RL realm to obtain compositionality and therefore zero-shot stitching across agents with multiple variation factors: i) random seed for the training; ii) environment style (background color); iii) training algorithm used (PPO and DDQN)

Luca Moschella

ELLIS Ph.D. in Computer Science

I’m excited by many Computer Science fields and, in particular, by Artificial Intelligence.