Published February 1, 2020
| Accepted Version
Discussion Paper
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Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning
- Creators
- Qu, Guannan
- Wierman, Adam
Chicago
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Abstract
We consider a general asynchronous Stochastic Approximation (SA) scheme featuring a weighted infinity-norm contractive operator, and prove a bound on its finite-time convergence rate on a single trajectory. Additionally, we specialize the result to asynchronous
Additional Information
© 2020 G. Qu & A. Wierman. to appear in Proceedings of Machine Learning Research.Attached Files
Accepted Version - 2002.00260.pdf
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2002.00260.pdf
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Additional details
- Eprint ID
- 101300
- Resolver ID
- CaltechAUTHORS:20200214-105555380
- Created
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2020-02-14Created from EPrint's datestamp field
- Updated
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2023-06-02Created from EPrint's last_modified field