Zero-Shot Stitching in Reinforcement Learning using Relative Representations
arxiv(2024)
摘要
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.
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