BooVI: Provably Efficient Bootstrapped Value Iteration
Authors: Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | While this paper is mainly on algorithmic and theoretical aspects of the bootstrapping idea in RL, it would be interesting to see the empirical strength of Boo VI in more challenging RL environments. We leave this part to our future work. |
| Researcher Affiliation | Academia | Northwestern University; boyiliu2018@u.northwestern.edu Northwestern University; qicai2022@u.northwestern.edu Princeton University; zy6@princeton.edu Northwestern University; zhaoranwang@gmail.com |
| Pseudocode | Yes | Algorithm 1 Bootstrapped Value Iteration (Boo VI) ... Algorithm 2 Bootstrapping Action-Value Function |
| Open Source Code | No | The paper does not provide any specific repository links or explicit statements about code availability. It mentions: 'We leave this part to our future work.' |
| Open Datasets | No | The paper is primarily theoretical and does not report using a specific publicly available dataset for empirical evaluation. It refers to illustrative experiments in Appendix F, which is not provided, and states 'We leave this part to our future work' regarding empirical strength. |
| Dataset Splits | No | The paper is primarily theoretical and does not provide specific dataset split information. |
| Hardware Specification | No | The paper is primarily theoretical and does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is primarily theoretical and does not provide specific software names with version numbers. |
| Experiment Setup | No | The paper is primarily theoretical and does not contain specific experimental setup details like hyperparameter values or training configurations. |