Reinforcement Learning with Action-Free Pre-Training from Videos

• URL: http://arxiv.org/abs/2203.13880v1
• Date: Fri, 25 Mar 2022 19:44:09 GMT
• Title: Reinforcement Learning with Action-Free Pre-Training from Videos
• Authors: Younggyo Seo, Kimin Lee, Stephen James, Pieter Abbeel
• Abstract summary: We introduce a framework that learns representations useful for understanding the dynamics via generative pre-training on videos. Our framework significantly improves both final performances and sample-efficiency of vision-based reinforcement learning.
• Score: 95.25074614579646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent unsupervised pre-training methods have shown to be effective on language and vision domains by learning useful representations for multiple downstream tasks. In this paper, we investigate if such unsupervised pre-training methods can also be effective for vision-based reinforcement learning (RL). To this end, we introduce a framework that learns representations useful for understanding the dynamics via generative pre-training on videos. Our framework consists of two phases: we pre-train an action-free latent video prediction model, and then utilize the pre-trained representations for efficiently learning action-conditional world models on unseen environments. To incorporate additional action inputs during fine-tuning, we introduce a new architecture that stacks an action-conditional latent prediction model on top of the pre-trained action-free prediction model. Moreover, for better exploration, we propose a video-based intrinsic bonus that leverages pre-trained representations. We demonstrate that our framework significantly improves both final performances and sample-efficiency of vision-based RL in a variety of manipulation and locomotion tasks. Code is available at https://github.com/younggyoseo/apv.

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