Abstract
Visual Reinforcement Learning is a popular and powerful framework that takes full advantage of the Deep Learning breakthrough. However, it is also known that variations in the input (e.g., different colors of the panorama due to the season of the year) or the task (e.g., changing the speed limit for a car to respect) could require complete retraining of the agents. In this work, we leverage recent developments in unifying latent representations to demonstrate that it is possible to combine the components of an agent, rather than retrain it from scratch. We build upon the recent relative representations framework and adapt it for Visual RL. This allows us to create completely new agents capable of handling environment-task combinations never seen during training. Our work paves the road toward a more accessible and flexible use of reinforcement learning.
Abstract (translated)
视觉强化学习是一个受欢迎且强大的框架，它充分利用了深度学习的突破。然而，值得注意的是，输入（例如，由于季节不同而出现全景的不同颜色）或任务（例如，改变汽车的速度限制以尊重交通规则）的差异可能需要对代理进行完整的重新训练。在这项工作中，我们利用最近统一 latent 表示的研究成果，证明将代理的各个组件结合在一起是可能的，而不是从头开始重新训练它。我们在视觉强化学习的基础上进行了改编，使其能够创建具有处理训练环境中从未见过的环境-任务组合的新代理。我们的工作为使用强化学习实现更加易用和灵活的目标铺平了道路。
URL
https://arxiv.org/abs/2404.12917
