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

2025-02-19

AI Summary

This work addresses the challenges of modeling large-scale (hundreds-of-units), terrain-aware, and temporally dependent collaborative decision-making in Hex-based wargaming simulations. Methodologically, we propose an RNN-AlphaZero hybrid architecture: (i) a wargame-semantics-aware state/action representation integrating multi-scale map encoding and sparse-reward modeling; and (ii) a customized recurrent neural network replacing the standard CNN backbone to enhance temporal dependency capture and cross-map-size generalization. Experiments demonstrate rapid convergence on canonical tactical scenarios, outperforming rule-based engines significantly, and validate strong scalability on large, variable-dimension maps with hundreds of units. The core contribution is the first deep integration of structured RNNs into the AlphaZero framework—enabling effective learning over high-dimensional hybrid discrete-continuous action spaces and long-horizon strategic dependencies.

Technology Category

Multiagent Systems: Adversarial AgentsGame Theory and Economic Paradigms: Adversarial LearningCognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Addresses strategic complexity in Hex and Counter Wargames
• Integrates Recurrent Neural Networks with AlphaZero
• Develops tailored Neural Network architecture for game environments

Innovation

• Reinforcement Learning integration
• Recurrent Neural Networks application
• Tailored game environment representations