Foundational Aspects of Machine Learning in Multi-Agent Online Games as Serious Games

Abstract

This seedling project investigated the complexities of scaling reinforcement learning algorithms, using commercial-off-the-shelf strategy games as the experimental environment. A major research contribution is an multi-agent reinforcement learning (MARL) approach where an agent learns policies based on other agents, rather than attempting to learn policy by itself, independent of the other agents. The research team also investigated using the Grey Wolf Optimizer simulate human-AI teaming and training, as a simpler method to approximate training conditions with hybrid teams. The project produced three papers and two patent submissions. In addition, software developed for the experiment is openly hosted on GitHub.

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Document Details

Document Type
Technical Report
Publication Date
Aug 05, 2021
Accession Number
AD1152078

Entities

People

  • Sungwon Yi

Organizations

  • Electronics and Telecommunications Research Institute

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Communication Systems
  • Computer Programs
  • Computer Vision
  • Engineering
  • Environment
  • Information Processing
  • Information Systems
  • Learning
  • Machine Learning
  • Multiagent Systems
  • Neural Networks
  • Reinforcement Learning
  • Simulations
  • Social Sciences
  • Training

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.

Technology Areas

  • AI & ML