Mask-based Predictive Representations for Reinforcement Learning
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 from the environment and the context information in the sequence to predict the masked information, thereby strengthening the agent's understanding of...
| Authors | Kai Zhao |
|---|---|
| Primary category | cs.LG |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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