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 [1].
Multi-objective Linear Reinforcement Learning with Lexicographic Rewards
Authors: Bo Xue, Dake Bu, Ji Cheng, Yuanyu Wan, Qingfu Zhang
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To bridge this gap, we examine MORL under lexicographic reward structures... We introduce the ๏ฌrst MORL algorithm with provable regret guarantees... Our work provides a comprehensive theoretical analysis of regret bounds... This work establishes the ๏ฌrst theoretical regret guarantee for MORL. |
| Researcher Affiliation | Academia | 1Department of Computer Science, City University of Hong Kong, Hong Kong, China 2School of Software Technology, Zhejiang University, Ningbo, China. Correspondence to: Qingfu Zhang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Lexicographic Linear Reinforcement Learning Algorithm 2 Lexicographic Action Elimination |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that open-source code for the described methodology is available. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and regret analysis. It does not conduct empirical studies using specific datasets that would be made publicly available. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments on datasets, therefore no dataset splits are provided. |
| Hardware Specification | No | The paper describes a theoretical algorithm and provides proofs; it does not include any experimental evaluation requiring specific hardware, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not present experimental results, therefore it does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm development and regret analysis. It does not describe an experimental setup with hyperparameters or training configurations. |