Deep Reinforcement Learning Solution of Reach-Avoid Games with Superior Evader in the Context of Unmanned Aerial Systems-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning Solution of Reach-Avoid Games with Superior Evader in the Context of Unmanned Aerial Systems

Published:2023-06-06 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Unmanned Aircraft Systems (ICUAS)
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Author:

Silveira Jefferson¹,Cabral Kleber²,Rabbath Camille-Alain³,Givigi Sidney²

Affiliation:

1. Queen’s University,Department of Electrical & Computer Engineering and the Ingenuity Labs Research Institute,Kingston,Canada,K7L 3N6

2. Queen’s University,School of Computing and the Ingenuity Labs Research Institute,Kingston,Canada,K7L 3N6

3. Defence Research and Development Canada,Valcartier,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10155645/10155789/10156154.pdf?arnumber=10156154

Reference22 articles.

1. Decomposition of Reachable Sets and Tubes for a Class of Nonlinear Systems

2. Optimal strategies for the lifeline differential game with limited lifetime

3. Reach-Avoid Games Via Mixed-Integer Second-Order Cone Programming

4. Reach-Avoid Verification for Nonlinear Systems Based on Boundary Analysis

5. A learning invader for the guarding a territory game

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2. Sufficient Convergence Conditions for Conflict Controlled Objects with Different Inertia;Cybernetics and Systems Analysis;2024-07

3. Control Strategies in the Problem of Approach of Conflict-Controlled Objects;Cybernetics and Systems Analysis;2024-03

4. An efficient reinforcement learning scheme for the confinement escape problem;Applied Soft Computing;2024-02

5. Multiple Equal-Speed Players in Reach-Avoid Differential Games: Non-point Capture;Lecture Notes in Electrical Engineering;2024

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