  Visual Model-Based Reinforcement Learning (MBRL) promises to encapsulate
agent's knowledge about the underlying dynamics of the environment, enabling
learning a world model as a useful planner. However, top MBRL agents such as
Dreamer often struggle with visual pixel-based inputs in the presence of
exogenous or irrelevant noise in the observation space, due to failure to
capture task-specific features while filtering out irrelevant spatio-temporal
details. To tackle this problem, we apply a spatio-temporal masking strategy, a
bisimulation principle, combined with latent reconstruction, to capture
endogenous task-specific aspects of the environment for world models,
effectively eliminating non-essential information. Joint training of
representations, dynamics, and policy often leads to instabilities. To further
address this issue, we develop a Hybrid Recurrent State-Space Model (HRSSM)
structure, enhancing state representation robustness for effective policy
learning. Our empirical evaluation demonstrates significant performance
improvements over existing methods in a range of visually complex control tasks
such as Maniskill \cite{gu2023maniskill2} with exogenous distractors from the
Matterport environment. Our code is avaliable at
https://github.com/bit1029public/HRSSM.
