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

TL;DR

This paper presents a novel reinforcement learning system combining Recurrent Neural Networks and AlphaZero to master complex Hex and Counter Wargames, addressing their strategic intricacies and scalability challenges.

Contribution

It introduces a new neural network architecture and state-action representations tailored for complex wargame environments, enabling effective learning with minimal training.

Findings

Promising results in typical scenarios

Generalization across different terrains and tactics

Potential scalability to larger maps

Abstract

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.