Learning Latent Dynamic Robust Representations for World Models
<|reference_start|>Learning Latent Dynamic Robust Representations for World Models: 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.<|reference_end|>
@article{sun2024learning,
    title={Learning Latent Dynamic Robust Representations for World Models},
    author={Ruixiang Sun, Hongyu Zang, Xin Li, Riashat Islam},
    journal={ICML 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06263},
    primaryClass={cs.LG cs.AI}
}