Variational Empowerment

Variational empowerment is a framework in reinforcement learning that aims to enable agents to learn complex skills and behaviors without relying on explicit reward signals. Current research focuses on improving the tractability of empowerment calculations, often through hierarchical architectures and variational methods, and applying it to diverse tasks like robotics and navigation. This intrinsic motivation approach holds significant promise for developing more autonomous and adaptable agents capable of lifelong learning and tackling complex, real-world problems where defining explicit rewards is challenging or impossible. The resulting advancements in unsupervised skill discovery and efficient exploration are driving progress in artificial intelligence.