Latent Action Space

Latent action spaces represent a powerful approach in reinforcement learning and related fields, aiming to simplify complex control problems by learning lower-dimensional representations of actions. Current research focuses on developing efficient algorithms for learning these representations, often employing techniques like variational autoencoders, normalizing flows, and diffusion models, and applying them to diverse tasks including robotics, dialogue systems, and world modeling. This approach improves sample efficiency, generalizability, and planning capabilities in high-dimensional action spaces, leading to more robust and efficient learning in various applications.