Learning Latent Dynamic Robust Representations for World Models

• URL: http://arxiv.org/abs/2405.06263v2
• Date: Thu, 30 May 2024 09:40:02 GMT
• Title: Learning Latent Dynamic Robust Representations for World Models
• Authors: Ruixiang Sun, Hongyu Zang, Xin Li, Riashat Islam,
• Abstract summary: Visual Model-Based Reinforcement Learning (MBL) promises to agent's knowledge about the underlying dynamics of the environment. Top-temporal agents such as Dreamer often struggle with visual pixel-based inputs in the presence of irrelevant noise in the observation space. We apply a-temporal masking strategy, combined with latent reconstruction, to capture endogenous task-specific aspects of the environment for world models.
• Score: 9.806852421730165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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