IEEE

Accelerating Residual Reinforcement Learning With Uncertainty Estimation

Abstract: Residual Reinforcement Learning (RL) is a popular approach for adapting pretrained policies by learning a lightweight residual policy that provides corrective actions. While Residual RL is ...
Microsoft

Accelerating protein engineering with fitness landscape modeling and reinforcement learning

Protein engineering holds significant promise for designing proteins with customized functions, yet the vast landscape of potential mutations versus limited lab capacity constrains the discovery of ...
IEEE

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

The rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for ...
GitHub

VLAC: A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning

VLAC is a general-purpose pair-wise critic and manipulation model which designed for real world robot reinforcement learning and data refinement. It provides robust evaluation capabilities for task ...