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.

翻译：智能体利用内部世界模型在不同尺度上推理和预测其多种行动轨迹。设计能够让机器学习多时间抽象层次的世界模型、同时处理复杂不确定性预测的学习范式与架构，是一项重大技术挑战。本文提出了一种用于学习多时间尺度世界模型的概率形式化框架，命名为多时间尺度状态空间模型（MTS3）。该模型采用一种计算高效的多时间尺度推理方案，能够对未来数秒内的长期预测和不确定性估计实现高精度。我们的实验聚焦于动作条件长期未来预测，结果表明MTS3在多个系统辨识基准测试中（包括复杂的仿真与真实世界动力学系统）均优于近期方法。代码已开源：https://github.com/ALRhub/MTS3。