Two new arXiv papers delve into the theoretical underpinnings of temporal-difference (TD) learning methods, focusing on their finite-iteration behavior and online statistical inference. The first paper by Ege Can Kaya analyzes asynchronous categorical TD learning in both scalar and multivariate settings, establishing discounted bounds and finite-iteration guarantees. The second paper examines online inference for functionals of the return distribution, proving that root-T errors converge weakly to Gaussian random elements and justifying bootstrap inference for various statistical functionals. AI
IMPACT These papers advance the theoretical understanding of temporal-difference learning, potentially leading to more robust and efficient reinforcement learning algorithms.
RANK_REASON Two academic papers published on arXiv detailing theoretical advancements in machine learning algorithms.
- alphaXiv
- arXiv
- CatalyzeX
- Cramér space
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- Gotit.pub
- Hugging Face
- Polyak--Ruppert
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- Cramér geometry
- Ege Can Kaya
- maximum mean discrepancy geometry
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