Hybrid Reinforcement Learning Control for a Micro Quadrotor Flight

2021 ◽  
Vol 5 (2) ◽  
pp. 505-510
Author(s):  
Jaehyun Yoo ◽  
Dohyun Jang ◽  
H. Jin Kim ◽  
Karl H. Johansson
Keyword(s):  
Reinforcement Learning ◽  
Learning Control ◽  
Hybrid Reinforcement

Deep reinforcement learning enabled self-learning control for energy efficient driving

2019 ◽  
Vol 99 ◽  
pp. 67-81 ◽  
Author(s):  
Xuewei Qi ◽  
Yadan Luo ◽  
Guoyuan Wu ◽  
Kanok Boriboonsomsin ◽  
Matthew Barth
Keyword(s):  
Reinforcement Learning ◽  
Energy Efficient ◽  
Learning Control ◽  
Self Learning

scholarly journals Comparing quantum hybrid reinforcement learning to classical methods

2021 ◽  
Author(s):  
Maximilian Moll ◽  
Leonhard Kunczik
Keyword(s):  
Reinforcement Learning ◽  
Quantum Circuits ◽  
Memory Usage ◽  
Computational Power ◽  
Classical Domain ◽  
Q Learning ◽  
Optimal Behavior ◽  
Computational Speed ◽  
Complex Decision ◽  
Hybrid Reinforcement

AbstractIn recent history, reinforcement learning (RL) proved its capability by solving complex decision problems by mastering several games. Increased computational power and the advances in approximation with neural networks (NN) paved the path to RL’s successful applications. Even though RL can tackle more complex problems nowadays, it still relies on computational power and runtime. Quantum computing promises to solve these issues by its capability to encode information and the potential quadratic speedup in runtime. We compare tabular Q-learning and Q-learning using either a quantum or a classical approximation architecture on the frozen lake problem. Furthermore, the three algorithms are analyzed in terms of iterations until convergence to the optimal behavior, memory usage, and runtime. Within the paper, NNs are utilized for approximation in the classical domain, while in the quantum domain variational quantum circuits, as a quantum hybrid approximation method, have been used. Our simulations show that a quantum approximator is beneficial in terms of memory usage and provides a better sample complexity than NNs; however, it still lacks the computational speed to be competitive.

scholarly journals Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments

2020 ◽  
Vol 6 (4) ◽  
pp. 1335-1349
Author(s):  
Charles E. Thornton ◽  
Mark A. Kozy ◽  
R. Michael Buehrer ◽  
Anthony F. Martone ◽  
Kelly D. Sherbondy
Keyword(s):  
Reinforcement Learning ◽  
Learning Control ◽  
Radar Detection ◽  
Detection And Tracking
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