Provably Efficient Algorithms for Multi-Objective Competitive RL
Authors: Tiancheng Yu, Yi Tian, Jingzhao Zhang, Suvrit Sra
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 <suvrit@mit.edu>. |
| 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. |