# Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks

arXiv: https://arxiv.org/abs/2505.14459 · HTML: https://mlsanity.com/paper/2505.14459/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 20 May 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [Reward Constrained Policy Optimization](https://mlsanity.com/paper/1805.11074/) (2018)
- [Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients](https://mlsanity.com/paper/1912.04871/) (2019)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization](https://mlsanity.com/paper/1709.07080/) (2017)
- [Towards Safe Load Balancing based on Control Barrier Functions and Deep Reinforcement Learning](https://mlsanity.com/paper/2401.05525/) (2024)
- [Safe Load Balancing in Software-Defined-Networking](https://mlsanity.com/paper/2410.16846/) (2024)

## Built on by (most cited first, 1 of 1)

- [KAN-Dreamer: Benchmarking Kolmogorov-Arnold Networks as Function Approximators in World Models](https://mlsanity.com/paper/2512.07437/) (2025)

## Abstract (arXiv)

Reinforcement learning (RL) has been increasingly applied to network control problems, such as load balancing. However, existing RL approaches often suffer from lack of interpretability and difficulty in extracting controller equations. In this paper, we propose the use of Kolmogorov-Arnold Networks (KAN) for interpretable RL in network control. We employ a PPO agent with a 1-layer actor KAN model and an MLP Critic network to learn load balancing policies that maximise throughput utility, minimize loss as well as delay. Our approach allows us to extract controller equations from the learned neural networks, providing insights into the decision-making process. We evaluate our approach using different reward functions demonstrating its effectiveness in improving network performance while providing interpretable policies.