  Intelligent agents use internal world models to reason and make predictions
about different courses of their actions at many scales. Devising learning
paradigms and architectures that allow machines to learn world models that
operate at multiple levels of temporal abstractions while dealing with complex
uncertainty predictions is a major technical hurdle. In this work, we propose a
probabilistic formalism to learn multi-time scale world models which we call
the Multi Time Scale State Space (MTS3) model. Our model uses a computationally
efficient inference scheme on multiple time scales for highly accurate
long-horizon predictions and uncertainty estimates over several seconds into
the future. Our experiments, which focus on action conditional long horizon
future predictions, show that MTS3 outperforms recent methods on several system
identification benchmarks including complex simulated and real-world dynamical
systems. Code is available at this repository: https://github.com/ALRhub/MTS3.
