Advisor(s)
Arie Nakhmani
Committee Member(s)
Amy Amara
Earl Wells
Karthikeyan Lingasubramanian
Leon Jololian
Document Type
Dissertation
Date of Award
2020
Abstract
We propose a self-supervised, model-free deep reinforcement learning architecture with cascade reward for unmanned aerial vehicle navigation and target interception in a 3D environment. The first contribution of the dissertation solves the problem of partial observability when non-linear function approximators are used for learning stochastic policies. The second contribution optimizes the problem of maximizing the total expected rewards. The third contribution trains the agent in a photo-realistic environment with a real physics engine. To achieve these goals, a deep Q-network that combines double and dueling architectures is adopted as a value function approximator, and the prioritized experience replays the sample independent and identically distributed random variables from the experiences with the highest relevance to the interception and obstacle avoidance tasks. A new simulator is developed for training drones to track and intercept a target. We demonstrate that our approach achieves better results in learning policies compared to state-of-the-art deep Q-network algorithms.
ProQuest Publication Number
ISBN
9798645468705
Recommended Citation
Darwish, Ali Alhaj, "Uav Navigation, Tracking, And Interception Using Deep Reinforcement Learning" (2020). All ETDs from UAB. 997.
https://digitalcommons.library.uab.edu/etd-collection/997
Comments
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