Deep lifelong reinforcement learning for resilient control and coordination

Abstract

The objective of this effort was to develop deep lifelong learning methods that can successfully handle sequential decision making in complex dynamic environments, focusing on multi-agent intelligence, surveillance, and reconnaissance (ISR) scenarios. We developed a novel architecture for deep convolutional neural networks that supports lifelong learning via deconvolutional factorization (DF-CNN), explored a combination of policy distillation via Distral and Sobolev training, and developed a hybrid controller for applying deep learning to ISR agents. Our approaches were evaluated on standard benchmark deep learning datasets, the DOOM environment, and on ISR scenarios in the ATE3 simulation environment.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2019
Accession Number
AD1074259

Entities

People

  • Eric Eaton
  • James Stokes
  • Seungwon Lee

Organizations

  • University of Pennsylvania

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computational Science
  • Computer Languages
  • Computer Vision
  • Convolutional Neural Networks
  • Deep Learning
  • Dimensionality Reduction
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Reinforcement Learning
  • Unmanned Aerial Vehicles

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Neural Network Machine Learning.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks