Summary of Statistical Efficiency Of Distributional Temporal Difference Learning and Freedman’s Inequality in Hilbert Spaces, by Yang Peng et al.
Statistical Efficiency of Distributional Temporal Difference Learning and Freedman’s Inequality in Hilbert Spaces
by Yang Peng, Liangyu Zhang, Zhihua Zhang
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates distributional reinforcement learning (DRL) and its applications to policy evaluation. Specifically, it focuses on the non-asymptotic statistical rates of distributional temporal difference learning (DTDL), a key component in DRL. The authors propose a novel algorithm called non-parametric distributional TD (NTD), which extends classic temporal difference learning (TD). They demonstrate that NTD requires ({-2}{}{-1}(1-){-3}) interactions with the environment to achieve an -optimal estimator in a tabular Markov decision process. This bound is minimax optimal up to logarithmic factors. The authors also revisit categorical distributional TD (CTD) and show that it has similar non-asymptotic convergence bounds. Furthermore, they extend their analysis to the Markovian setting, proposing variance-reduced variants of NTD and CTD. These algorithms achieve a sample complexity bound of (^{-2}{,}{-1}(1-)^{-3}+t_{mix}_{,}{-1}(1-){-1}). To establish these sharp statistical rates, the authors develop a novel Freedman’s inequality in Hilbert spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers learn to make decisions. It focuses on a specific part of this process called policy evaluation. The researchers propose new ways to do policy evaluation using something called distributional temporal difference learning. They show that their method is efficient and can be used with different types of decision-making problems. The authors also compare their approach to another popular method called categorical distributional TD. They find that both methods have similar performance, but the new method is more flexible. This work has implications for how we design algorithms for complex decision-making tasks. |
Keywords
* Artificial intelligence * Reinforcement learning
