Scaling Reinforcement Learning through Feudal Multi-Agent Hierarchy

Abstract

Militaries conduct wargames for training, planning, and research purposes. Artificial intelligence (AI)can improve military wargaming by reducing costs, speeding up the decision-making process, and offering new insights. Previous researchers explored using reinforcement learning (RL) for wargaming based on the successful use of RL for other human competitive games. While previous research has demonstrated that an RL agent can generate combat behavior, those experiments have been limited to small-scale wargames. This thesis investigates the feasibility and acceptability of scaling hierarchical reinforcement learning (HRL) to support integrating AI into large military wargames. Additionally, this thesis also investigates potential complications that arise when replacing the opposing force with an intelligent agent by exploring the ways in which an intelligent agent can cause a wargame to fail.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2022
Accession Number
AD1201723

Entities

People

  • Patrick R Rood

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Cyber
  • Engineered Resilient Systems
  • Human Systems
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Command And Control
  • Computational Science
  • Computers
  • Data Mining
  • Dimensionality Reduction
  • Experimental Design
  • Information Processing
  • Information Science
  • Information Systems
  • Intelligent Agents
  • Machine Learning
  • Military Science
  • Multiagent Systems
  • Neural Networks
  • Probabilistic Models
  • Warfare

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Game Theory.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy