# Reinforcement Learning the Chromatic Symmetric Function

arXiv: https://arxiv.org/abs/2410.19189 · HTML: https://mlsanity.com/paper/2410.19189/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 1 of 1)

- [Constructions in combinatorics via neural networks](https://mlsanity.com/paper/2104.14516/) (2021)

## Abstract (arXiv)

We propose a conjectural counting formula for the coefficients of the chromatic symmetric function of unit interval graphs using reinforcement learning. The formula counts specific disjoint cycle-tuples in the graphs, referred to as Eschers, which satisfy certain concatenation conditions. These conditions are identified by a reinforcement learning model and are independent of the particular unit interval graph, resulting a universal counting expression.