Zero-shot stitching in Reinforcement Learning using Relative RepresentationsDownload PDF

Published: 20 Jul 2023, Last Modified: 01 Sept 2023EWRL16Readers: Everyone
Keywords: visual reinforcement learning, reinforcement learning, latent space translation, relative representation, zero-shot, stitching, latent communication
TL;DR: Extended abstract: we perform zero-shot stitching between policies and encoders trained on variations of the Car Racing environment
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)
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