A Note on Stability in Asynchronous Stochastic Approximation without Communication Delays
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913467592605696 |
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| author | Yu, Huizhen Wan, Yi Sutton, Richard S. |
| author_facet | Yu, Huizhen Wan, Yi Sutton, Richard S. |
| contents | In this paper, we study asynchronous stochastic approximation algorithms without communication delays. Our main contribution is a stability proof for these algorithms that extends a method of Borkar and Meyn by accommodating more general noise conditions. We also derive convergence results from this stability result and discuss their application in important average-reward reinforcement learning problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_15091 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Note on Stability in Asynchronous Stochastic Approximation without Communication Delays Yu, Huizhen Wan, Yi Sutton, Richard S. Machine Learning Optimization and Control 62L20 (Primary) 93E35, 90C40 (Secondary) In this paper, we study asynchronous stochastic approximation algorithms without communication delays. Our main contribution is a stability proof for these algorithms that extends a method of Borkar and Meyn by accommodating more general noise conditions. We also derive convergence results from this stability result and discuss their application in important average-reward reinforcement learning problems. |
| title | A Note on Stability in Asynchronous Stochastic Approximation without Communication Delays |
| topic | Machine Learning Optimization and Control 62L20 (Primary) 93E35, 90C40 (Secondary) |
| url | https://arxiv.org/abs/2312.15091 |