Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling

TL;DR

This paper introduces a latent space approach to analyze and address the challenges faced by model-based reinforcement learning methods when transferring policies from simulation to real-world environments, highlighting persistent gaps.

Contribution

It extends model-based RL with latent space modeling to better understand and mitigate the sim-to-real transfer challenges.

Findings

Latent space analysis reveals key factors affecting transfer performance.

The proposed method helps measure the sim-to-real gap effectively.

Experiments show ongoing challenges in bridging the sim-to-real gap for model-based RL.

Abstract

Reinforcement learning (RL) is playing an increasingly important role in fields such as robotic control and autonomous driving. However, the gap between simulation and the real environment remains a major obstacle to the practical deployment of RL. Agents trained in simulators often struggle to maintain performance when transferred to real-world physical environments. In this paper, we propose a latent space based approach to analyze the impact of simulation on real-world policy improvement in model-based settings. As a natural extension of model-based methods, our approach enables an intuitive observation of the challenges faced by model-based methods in sim-to-real transfer. Experiments conducted in the MuJoCo environment evaluate the performance of our method in both measuring and mitigating the sim-to-real gap. The experiments also highlight the various challenges that remain in overcoming the sim-to-real gap, especially for model-based methods.