Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Provably Efficient Algorithms for Multi-Objective Competitive RL
Authors: Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,536 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,742 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work provides the first provably efficient algorithms for vector-valued Markov games and our theoretical guarantees are near-optimal. |
| Researcher Affiliation | Academia | Tiancheng Yu 1 Yi Tian 1 Jingzhao Zhang 1 Suvrit Sra 1 1Department of EECS, MIT, Cambridge, USA. Correspondence to: Suvrit Sra <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Multi-objective Meta-algorithm (MOMA) Algorithm 2 VI-Hoeffding (VI-HOEFFDING) Algorithm 3 VI-BERNSTEIN |
| Open Source Code | No | The paper does not contain any statements about releasing source code, nor does it provide links to any code repositories for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical algorithms and their guarantees and does not involve experiments with specific datasets, therefore no public dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe a concrete experimental setup with hyperparameters or training configurations. |