Statistical Evaluation of Multi-Agent Reinforcement Learning Models Under Different Versions of TensorFlow

Abstract

The Agents Leveraging Learning for Intelligent Engagement with Soldiers (ALLIES) team has been using TensorFlow, a machine learning software library, to train and evaluate agents in tasks such as a continuous 2D version of the Predator-Prey Pursuit game. These tasks have provided a practical, dynamic research environment for studying cooperation and competition in agent-agent and human-agent teams, but staying compatible with research partners requires that we update to a new release of TensorFlow. To maintain continuity in our research, we evaluated predator-prey data created under the original and the new release of TensorFlow and confirmed that this transition does not affect training and evaluation.

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Document Details

Document Type
Technical Report
Publication Date
Mar 22, 2021
Accession Number
AD1125841

Entities

People

  • Derrik Asher
  • Erin Zaroukian
  • Rolando Fernandez

Organizations

  • United States Army

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Availability
  • Classification
  • Collisions
  • Competition
  • Continuity
  • Contracts
  • Cooperation
  • Data Science
  • Data Sets
  • Education
  • Environment
  • Information Science
  • Learning
  • Machine Learning
  • Military Research
  • Monitoring
  • Neural Networks
  • Probability Distributions
  • Reinforcement Learning
  • Security
  • Spatial Distribution
  • Test And Evaluation
  • Training

Fields of Study

  • Computer science

Readers

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  • Enterprise Information Systems Architecture and Joint Command Capability Interoperability Support.
  • Solar Photovoltaics and Thermoelectric Devices.

Technology Areas

  • AI & ML