Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning
Authors: Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper initiates the theoretical study of policy finetuning... We study the policy finetuning problem theoretically in finite-horizon Markov Decision Processes (MDPs) with H time steps, S states, and A actions. |
| Researcher Affiliation | Collaboration | Tengyang Xie UIUC tx10@illinois.edu Nan Jiang UIUC nanjiang@illinois.edu Huan Wang Salesforce Research huan.wang@salesforce.com Caiming Xiong Salesforce Research cxiong@salesforce.com Yu Bai Salesforce Research yu.bai@salesforce.com |
| Pseudocode | Yes | Algorithm 1 Pessimistic Value Iteration with Reference-Advantage Decomposition (PEVI-ADV) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper studies theoretical aspects of Reinforcement Learning in episodic Markov Decision Processes (MDPs) and does not use or provide access information for any publicly available or open dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving dataset splits (e.g., training, validation, or test splits). |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments that would require or specify hardware details. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and their sample complexity, thus it does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and theoretical analysis, thus it does not provide specific experimental setup details such as hyperparameters or training configurations. |