arXiv:2607.04153v1 Announce Type: cross Abstract: Vision-based deep reinforcement learning involves dealing with high-dimensional inputs of image information. It is crucial to abstract effective states from high-dimensional image inputs and limited samples for sample-efficient reinforcement learning. To address this challenge, inspired by fields such as natural language processing and computer vision, we propose a self-supervised task based on mask prediction as an auxiliary task for reinforcement learning. This non-reconstruction method uses the sequence information collected by the agent fro
This research builds on recent advances in self-supervised learning from fields like natural language processing and computer vision, applying them to the challenging problem of sample efficiency in reinforcement learning.
Improving sample efficiency is critical for making deep reinforcement learning practical for real-world applications with high-dimensional inputs and limited data, potentially unlocking new capabilities in autonomous systems.
The proposed mask-based predictive representation method offers a novel approach to abstracting effective states from raw image data, potentially leading to more robust and data-efficient AI agents.
- · AI researchers
- · Robotics companies
- · Autonomous systems developers
- · Companies reliant on less efficient RL methods
Self-supervised learning techniques become more prevalent and effective in reinforcement learning, reducing data requirements for training.
More complex robotic tasks and real-world autonomous applications become feasible with improved sample efficiency.
The development of highly adaptive and general-purpose AI agents accelerates, impacting various industries including logistics and manufacturing.
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Read at arXiv cs.AI