Recurrent State Space Model

Recurrent State Space Models (RSSMs) are powerful tools for learning complex temporal dynamics from sequential data, aiming to build accurate and robust representations of the underlying system. Current research focuses on improving model parameterization for enhanced stability and efficiency, particularly addressing challenges in long-range prediction and handling changing dynamics through techniques like incorporating contextual information or latent factors. These advancements are significantly impacting model-based reinforcement learning and robotic control, enabling more robust and adaptable agents capable of zero-shot generalization and effective manipulation in complex environments.