Generalizing vision-based reinforcement learning (RL) agents to novel environments remains a difficult and open challenge. Current trends are to collect large-scale datasets or use data augmentation techniques to prevent overfitting and improve downstream generalization. However, the computational and data collection costs increase exponentially with the number of task variations and can destabilize the already difficult task of training RL agents. In this work, we take inspiration from recent advances in computational neuroscience and propose a model, Associative Latent DisentAnglement (ALDA), that builds on standard off-policy RL towards zero-shot generalization. Specifically, we revisit the role of latent disentanglement in RL and show how combining it with a model of associative memory achieves zero-shot generalization on difficult task variations without relying on data augmentation. Finally, we formally show that data augmentation techniques are a form of weak disentanglement and discuss the implications of this insight.

本研究解决了视觉基础强化学习（RL）代理在新环境中的泛化难题，提出了一种新的模型——关联潜在解耦（ALDA），它结合潜在解耦和联想记忆模型实现零样本泛化，避免了数据增强的依赖。研究表明，数据增强技术实际上是一种弱解耦形式，这一发现对强化学习的训练方法具有重要影响。

无数据增强的视觉基础强化学习的零样本泛化