# Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2502.13918 · HTML: https://mlsanity.com/paper/2502.13918/

## 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 9 of its 28 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## 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, 9 of 9)

- [Adaptive Computation Time for Recurrent Neural Networks](https://mlsanity.com/paper/1603.08983/) (2016)
- [Adaptive Neural Networks for Efficient Inference](https://mlsanity.com/paper/1702.07811/) (2017)
- [Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks](https://mlsanity.com/paper/2106.04537/) (2021)
- [Hierarchical Multiscale Recurrent Neural Networks](https://mlsanity.com/paper/1609.01704/) (2016)
- [End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking](https://mlsanity.com/paper/2202.05826/) (2022)
- [MEMO: A Deep Network for Flexible Combination of Episodic Memories](https://mlsanity.com/paper/2001.10913/) (2020)
- [HexagDLy - Processing hexagonally sampled data with CNNs in PyTorch](https://mlsanity.com/paper/1903.01814/) (2019)
- [Mastering Terra Mystica: Applying Self-Play to Multi-agent Cooperative Board Games](https://mlsanity.com/paper/2102.10540/) (2021)
- [Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning](https://mlsanity.com/paper/2109.02354/) (2021)

## Abstract (arXiv)

Hex and Counter Wargames are adversarial two-player simulations of real military conflicts requiring complex strategic decision-making. Unlike classical board games, these games feature intricate terrain/unit interactions, unit stacking, large maps of varying sizes, and simultaneous move and combat decisions involving hundreds of units. This paper introduces a novel system designed to address the strategic complexity of Hex and Counter Wargames by integrating cutting-edge advancements in Recurrent Neural Networks with AlphaZero, a reliable modern Reinforcement Learning algorithm. The system utilizes a new Neural Network architecture developed from existing research, incorporating innovative state and action representations tailored to these specific game environments. With minimal training, our solution has shown promising results in typical scenarios, demonstrating the ability to generalize across different terrain and tactical situations. Additionally, we explore the system's potential to scale to larger map sizes. The developed system is openly accessible, facilitating continued research and exploration within this challenging domain.