Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Nearly Optimal Policy Optimization with Stable at Any Time Guarantee
Authors: Tianhao Wu, Yunchang Yang, Han Zhong, Liwei Wang, Simon Du, Jiantao Jiao
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that our algorithm achieves e O(AH4K) regret. When S > H, our algorithm is minimax optimal when ignoring logarithmic factors. To our best knowledge, RPO-SAT is the first computationally efficient, nearly minimax optimal policy-based algorithm for tabular RL. |
| Researcher Affiliation | Academia | 1University of California, Berkeley 2Center for Data Science, Peking University 3Peng Cheng Laboratory 4Key Laboratory of Machine Perception, MOE, School of Artificial Intelligence, Peking University 5University of Washington. |
| Pseudocode | Yes | Algorithm 1 Reference-based Policy Optimization with Stable at Any Time guarantee (RPO-SAT) |
| Open Source Code | No | The paper does not provide any information or links regarding open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention specific datasets or their public availability for training. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |