Loading Now

Summary of Learning Latent Dynamic Robust Representations For World Models, by Ruixiang Sun et al.


Learning Latent Dynamic Robust Representations for World Models

by Ruixiang Sun, Hongyu Zang, Xin Li, Riashat Islam

First submitted to arxiv on: 10 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The proposed Visual Model-Based Reinforcement Learning (MBRL) approach aims to create a world model that can be used as a planner by encapsulating the agent’s knowledge about the environment. However, existing top-performing MBRL agents like Dreamer struggle with noisy pixel-based inputs due to their inability to filter out irrelevant details and capture task-specific features. To address this issue, the authors propose a spatio-temporal masking strategy, bisimulation principle, and latent reconstruction to capture endogenous task-specific aspects of the environment. They also introduce a Hybrid Recurrent State-Space Model (HRSSM) structure to enhance state representation robustness for effective policy learning. The approach is evaluated on visually complex control tasks like Maniskill with exogenous distractors from the Matterport environment, achieving significant performance improvements over existing methods.
Low GrooveSquid.com (original content) Low Difficulty Summary
Imagine you’re teaching a robot to navigate a complex environment by giving it hints about what’s going on around it. This is called reinforcement learning, and it’s important for robots to learn how the world works so they can make good decisions. The problem is that there’s often noise or distractions in the data the robot receives, which makes it hard for the robot to learn. To solve this problem, researchers developed a new approach that filters out irrelevant information and focuses on what matters most. They also created a special type of model that helps the robot make good decisions by learning from its mistakes. The results show that their approach works much better than existing methods in complex environments with distractions.

Keywords

» Artificial intelligence  » Reinforcement learning