Sample-Efficiency of Kolmogorov-Arnold Networks

Key points

• Kolmogorov-Arnold Networks are a recently proposed architecture that can learn physical relationships in control problems effectively, with significantly higher parameter efficiency and interpretability when compared to Multi-Layer-Perceptron architectures.1
• In this work, we systematically study sample-efficiency using computational experiments, covering the Feynman dataset and the Gymnasium RL benchmark.1
• The results show that similar performance can be achieved with 40% fewer samples using the Kolmogorov-Arnold architecture, and that relative performance improvements up to 50% occur during the training process.1
• These results highlight the potential of the Kolmogorov-Arnold architectures for more sample-efficient reinforcement learning.1

Sources (1)

• [1]Sample-Efficiency of Kolmogorov-Arnold Networks
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 11:17 AM

  Deep reinforcement learning has achieved substantial performance gains over classical control approaches.

  Kolmogorov-Arnold Networks are a recently proposed architecture that can learn physical relationships in control problems effectively, with significantly higher parameter efficiency and interpretability when compared to Multi-Layer-Perceptron architectures.

Extractive summary: sentences quoted from the sources.