Title :
Fidelity-Based Probabilistic Q-Learning for Control of Quantum Systems
Author :
Chunlin Chen ; Daoyi Dong ; Han-Xiong Li ; Jian Chu ; Tzyh-Jong Tarn
Author_Institution :
Dept. of Control & Syst. Eng., Nanjing Univ., Nanjing, China
Abstract :
The balance between exploration and exploitation is a key problem for reinforcement learning methods, especially for Q-learning. In this paper, a fidelity-based probabilistic Q-learning (FPQL) approach is presented to naturally solve this problem and applied for learning control of quantum systems. In this approach, fidelity is adopted to help direct the learning process and the probability of each action to be selected at a certain state is updated iteratively along with the learning process, which leads to a natural exploration strategy instead of a pointed one with configured parameters. A probabilistic Q-learning (PQL) algorithm is first presented to demonstrate the basic idea of probabilistic action selection. Then the FPQL algorithm is presented for learning control of quantum systems. Two examples (a spin-1/2 system and a Λ-type atomic system) are demonstrated to test the performance of the FPQL algorithm. The results show that FPQL algorithms attain a better balance between exploration and exploitation, and can also avoid local optimal policies and accelerate the learning process.
Keywords :
learning (artificial intelligence); probability; FPQL algorithm; fidelity based probabilistic Q-learning; learning process; probabilistic action selection; quantum system control; reinforcement learning methods; Approximation algorithms; Control systems; Educational institutions; Learning (artificial intelligence); Learning systems; Probabilistic logic; Probability distribution; Fidelity; probabilistic Q-learning; quantum control; reinforcement learning; reinforcement learning.;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2013.2283574