Solving Reward-Collecting Problems with UAVs: A Comparison of Online Optimization and Q-Learning

  title={Solving Reward-Collecting Problems with UAVs: A Comparison of Online Optimization and Q-Learning},
  author={Yixuan Liu and Chrysafis Vogiatzis and Ruriko Yoshida and Erich Morman},
  journal={Journal of Intelligent \& Robotic Systems},
Uncrewed autonomous vehicles (UAVs) have made significant contributions to reconnaissance and surveillance missions in past US military campaigns. As the prevalence of UAVs increases, there has also been improvements in counter-UAV technology that makes it difficult for them to successfully obtain valuable intelligence within an area of interest. Hence, it has become important that modern UAVs can accomplish their missions while maximizing their chances of survival. In this work, we… 



Reinforcement Learning: A Tutorial Survey and Recent Advances

  • A. Gosavi
  • Computer Science
    INFORMS J. Comput.
  • 2009
This overview of reinforcement learning is aimed at uncovering the mathematical roots of this science so that readers gain a clear understanding of the core concepts and are able to use them in their own research.

Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting

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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning

This book examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques, and outlines the computational technology underlying these methods.

Deliberation for autonomous robots: A survey

Reinforcement Learning: An Introduction

This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.

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Playing Atari with Deep Reinforcement Learning

This work presents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning, which outperforms all previous approaches on six of the games and surpasses a human expert on three of them.

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A novel concept (Semi- Autonomous Victim Extraction Robot) designed to address the shortcomings of existing systems is described in the conclusion, along with detailed discussion on how it improves upon state of the art systems.

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This book provides detailed coverage of modelling decision processes under uncertainty, robustness, designing and estimating value function approximations, choosing effective step-size rules, and convergence issues and is an excellent textbook for advanced undergraduate and beginning graduate students.