Advisory Committee Chair
Arie Nakhmani
Advisory Committee Members
Amy Amara
Earl Wells
Karthikeyan Lingasubramanian
Leon Jololian
Document Type
Dissertation
Date of Award
2020
Degree Name by School
Doctor of Philosophy (PhD) School of Engineering
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.
Recommended Citation
Alhaj Darwish, Ali, "Uav Navigation, Tracking, And Interception Using Deep Reinforcement Learning" (2020). All ETDs from UAB. 997.
https://digitalcommons.library.uab.edu/etd-collection/997