Mastering the Digital Art of War: Developing Intelligent Combat Simulation Agents for Wargaming Using Hierarchical Reinforcement Learning

2024-08-23 · Scotty Black

In today's rapidly evolving military landscape, advancing artificial intelligence (AI) in support of wargaming becomes essential. Despite reinforcement learning (RL) showing promise for developing intelligent agents, conventional RL faces limitations in handling the complexity inherent in combat simulations. This dissertation proposes a comprehensive approach, including targeted observation abstractions, multi-model integration, a hybrid AI framework, and an overarching hierarchical reinforcement learning (HRL) framework. Our localized observation abstraction using piecewise linear spatial decay simplifies the RL problem, enhancing computational efficiency and demonstrating superior efficacy over traditional global observation methods. Our multi-model framework combines various AI methodologies, optimizing performance while still enabling the use of diverse, specialized individual behavior models. Our hybrid AI framework synergizes RL with scripted agents, leveraging RL for high-level decisions and scripted agents for lower-level tasks, enhancing adaptability, reliability, and performance. Our HRL architecture and training framework decomposes complex problems into manageable subproblems, aligning with military decision-making structures. Although initial tests did not show improved performance, insights were gained to improve future iterations. This study underscores AI's potential to revolutionize wargaming, emphasizing the need for continued research in this domain.

📄 PDF Abstract BibTeX arXiv:2408.13333

Code (0)

등록된 구현이 없습니다.

Tasks

Computational Efficiency Decision Making Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Scaling Intelligent Agents in Combat Simulations for Wargaming

2024-02-08 · Scotty Black, Christian Darken

Remaining competitive in future conflicts with technologically-advanced competitors requires us to accelerate our research and development in artificial intelligence (AI) for wargaming. More importantly, leveraging machi…

Deep Reinforcement Learning Hierarchical Reinforcement Learning Reinforcement Learning (RL)

Reinforcement Learning Environment with LLM-Controlled Adversary in D&D 5th Edition Combat

2025-03-19 · Joseph Emmanuel DL Dayo, Michel Onasis S. Ogbinar, Prospero C. Naval Jr

The objective of this study is to design and implement a reinforcement learning (RL) environment using D\&D 5E combat scenarios to challenge smaller RL agents through interaction with a robust adversarial agent controlle…

Decision Making Reinforcement Learning (RL)

A Hierarchical Hybrid AI Approach: Integrating Deep Reinforcement Learning and Scripted Agents in Combat Simulations

2025-11-28 · Scotty Black, Christian Darken arxiv

In the domain of combat simulations in support of wargaming, the development of intelligent agents has predominantly been characterized by rule-based, scripted methodologies with deep reinforcement learning (RL) approach…

Reinforcement Learning

Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach

2023-04-19 · Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama

This work contributes to developing an agent based on deep reinforcement learning capable of acting in a beyond visual range (BVR) air combat simulation environment. The paper presents an overview of building an agent re…

Deep Reinforcement Learning reinforcement-learning

Scaling Artificial Intelligence for Digital Wargaming in Support of Decision-Making