Summary of Mastering the Digital Art Of War: Developing Intelligent Combat Simulation Agents For Wargaming Using Hierarchical Reinforcement Learning, by Scotty Black
Mastering the Digital Art of War: Developing Intelligent Combat Simulation Agents for Wargaming Using Hierarchical Reinforcement Learning
by Scotty Black
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This dissertation proposes a comprehensive approach to advancing artificial intelligence (AI) in support of wargaming, leveraging reinforcement learning (RL) and hierarchical reinforcement learning (HRL). The authors introduce targeted observation abstractions, multi-model integration, and a hybrid AI framework that combines RL with scripted agents. They demonstrate the effectiveness of their approach using piecewise linear spatial decay, which simplifies the RL problem and improves computational efficiency. The authors also decompose complex problems into manageable subproblems, aligning with military decision-making structures. While initial tests did not show improved performance, the study underscores AI’s potential to revolutionize wargaming. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists are trying to improve artificial intelligence (AI) for use in war games. They’re using a type of learning called reinforcement learning (RL) and breaking it down into smaller parts to make it more efficient. The team is combining different AI approaches together, like using scripted agents for simple tasks and RL for big decisions. They’re also trying to make the AI work better by simplifying the problem it’s solving. Although their first tests didn’t show much improvement, they learned a lot that will help them do better in the future. |
Keywords
» Artificial intelligence » Reinforcement learning
