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..
Dynamic Regret of Policy Optimization in Non-Stationary Environments
Authors: Yingjie Fei, Zhuoran Yang, Zhaoran Wang, Qiaomin Xie
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our contributions. The contributions of our work can be summarized as follows: We propose two model-free policy optimization algorithms, POWER and POWER++, for non-stationary RL with adversarial rewards; We provide dynamic regret analysis for both algorithms, and the regret bounds are applicable across all regimes of non-stationarity of the underlying model; When the environment is nearly stationary, our dynamic regret bounds are of order O(T 1/2) and match the near-optimal static regret bounds, thereby demonstrating the adaptive nearoptimality of our algorithms in slow-changing environments. |
| Researcher Affiliation | Academia | Yingjie Fei1 Zhuoran Yang2 Zhaoran Wang1 Qiaomin Xie3 1 Northwestern University; EMAIL, EMAIL 2 Princeton University; EMAIL 3 Cornell University; EMAIL |
| Pseudocode | Yes | Algorithm 1 POWER; Algorithm 2 POWER++ |
| Open Source Code | No | The paper does not include any statement about providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention using any specific datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not specify any dataset splits (training, validation, test) as it does not conduct empirical experiments. |
| Hardware Specification | No | The paper focuses on theoretical analysis and algorithm design and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers needed for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameters or training configurations for empirical evaluation. |