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Acceleration of reinforcement learning by policy evaluation using nonstationary iterative method
- Source :
- IEEE transactions on cybernetics. 44(12):2696-2705
- Publication Year :
- 2014
- Publisher :
- IEEE, 2014.
-
Abstract
- Typical methods for solving reinforcement learning problems iterate two steps, policy evaluation and policy improvement. This paper proposes algorithms for the policy evaluation to improve learning efficiency. The proposed algorithms are based on the Krylov Subspace Method (KSM), which is a nonstationary iterative method. The algorithms based on KSM are tens to hundreds times more efficient than existing algorithms based on the stationary iterative methods. Algorithms based on KSM are far more efficient than they have been generally expected. This paper clarifies what makes algorithms based on KSM makes more efficient with numerical examples and theoretical discussions.
- Subjects :
- Mathematical optimization
Computer science
Iterative method
Krylov subspace
Models, Theoretical
Computer Science Applications
Decision Support Techniques
Feedback
Human-Computer Interaction
Acceleration
Control and Systems Engineering
Artificial Intelligence
Reinforcement learning
Computer Simulation
Electrical and Electronic Engineering
Reinforcement, Psychology
Software
Algorithms
Problem Solving
Information Systems
Subjects
Details
- Language :
- English
- ISSN :
- 21682275
- Volume :
- 44
- Issue :
- 12
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on cybernetics
- Accession number :
- edsair.doi.dedup.....de3e17c80f1893ef6c59ee2072aaf167