1029Lab

Multi-View Reinforcement Learning (MVRL) provides agents with multi-view observations to perceive environments more effectively. The goal is to extract compact and task-relevant latent representations from these views for use in control tasks. However, this is challenging due to redundant or distracting information and missing views.

Our work introduces Multi-view Fusion State for Control (MFSC), a method that uniquely integrates bisimulation metric learning into MVRL to learn better task-relevant representations. We also propose a multi-view-based mask and latent reconstruction auxiliary task that leverages shared information across views and enhances robustness to missing data.