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Table of Contents
Intro
The pipeline
Problem-solving approaches
Our problem-solving approach
Can we get the best of both worlds?
Our case study
Reinforcement learning setup
Code time!
Planning
Algorithm to the rescue
Value Iteration
Value Iteration Networks
Moving beyond known world-models
What about when we don’t know the MDP?
Bridging the gap between the algorithm and its application
Algorithmic bottleneck
Breaking the bottleneck
Breaking the bottleneck with GNNs
Synthetic data
Results
Why did it work?
Studying the executor
Studying the algorithmic bottleneck
Conclusions
Deploying-NAR conclusions
Deploying-NAR next steps
Summary
This document discusses Neural Algorithmic Reasoning and its deployment in real-world solutions. It explores the combination of classical algorithms with neural networks, focusing on Graph Neural Networks to imitate dynamic programming algorithms. The study introduces XLVIN to break the algorithmic bottleneck, showing empirical gains in low-data environments. The presentation concludes with insights on applying optimal algorithms without privileged information and the importance of data efficiency in planning.