5 accepted papers
Multi-task reinforcement learning (RL) aims to enhance agent performance across multiple tasks by enabling effective knowledge transfer. However, these methods adopt a fully shared policy across all tasks without explicitly distinguishing between related and conflicting ones, making them suffer from
Achieving strong policy generalization to unseen environments remains a core challenge in visual reinforcement learning, and segmenting task-relevant regions to mitigate the influence of irrelevant visual cues has emerged as a promising direction. However, existing methods rely solely on the current
Series Section Electron Microscopy (ssEM) is a crucial technique for visualizing three-dimensional (3D) biological structures, which involves collecting electron microscopy images from a series of biological sections along the z-axis and reconstructing the 3D structure. 3D registration is an essenti…
Multi-view representation learning integrates multiple observable views of an entity into a unified representation to facilitate downstream tasks. Current methods predominantly focus on distinguishing compatible components across views, followed by a single-step parallel fusion process. However, thi…
Proximal Policy Optimization (PPO) is an important reinforcement learning method, which has achieved great success in sequential decision-making problems. However, PPO faces the issue of sample inefficiency, which is due to the PPO cannot make use of off-policy data. In this paper, we propose an Off…