Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908818559991808 |
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| author | Srikant, R. |
| author_facet | Srikant, R. |
| contents | We prove a non-asymptotic central limit theorem for vector-valued martingale differences using Stein's method, and use Poisson's equation to extend the result to functions of Markov Chains. We then show that these results can be applied to establish a non-asymptotic central limit theorem for Temporal Difference (TD) learning with averaging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15719 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning Srikant, R. Probability Machine Learning Systems and Control Optimization and Control We prove a non-asymptotic central limit theorem for vector-valued martingale differences using Stein's method, and use Poisson's equation to extend the result to functions of Markov Chains. We then show that these results can be applied to establish a non-asymptotic central limit theorem for Temporal Difference (TD) learning with averaging. |
| title | Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning |
| topic | Probability Machine Learning Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2401.15719 |