Quantum Reinforcement Learning

A novel quantum reinforcement learning is proposed through combining quantum theory and reinforcement learning. Inspired by state superposition principle, a framework of state value update algorithm is introduced. The state/action value is represented with quantum state and the probability of action...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 686 - 689
Main Authors Dong, Daoyi, Chen, Chunlin, Chen, Zonghai
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Mobile Robot
Probability Amplitude
Quantum Computation
Reinforcement Learn
Reinforcement Learn Algorithm
Probabilistic approach
Reinforcement learning
Updating
Eigenvalue problem
Reward
Superposition principle
Modeling
Quantum theory
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Summary:A novel quantum reinforcement learning is proposed through combining quantum theory and reinforcement learning. Inspired by state superposition principle, a framework of state value update algorithm is introduced. The state/action value is represented with quantum state and the probability of action eigenvalue is denoted by probability amplitude, which is updated according to rewards. This approach makes a good tradeoff between exploration and exploitation using probability and can speed up learning. The results of simulated experiment verified its effectiveness and superiority.
ISBN:9783540283256
3540283250
3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539117_97