Sample-Efficient Reinforcement Learning of Partially Observable Markov Games
Authors: Qinghua Liu, Csaba Szepesvari, Chi Jin
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work is purely theoretical and we do not anticipate any potential negative social impacts. Did you run experiments? [N/A] |
| Researcher Affiliation | Collaboration | Qinghua Liu Princeton University qinghual@princeton.edu Csaba Szepesvári Deep Mind and University of Alberta szepesva@ualberta.ca Chi Jin Princeton University chij@princeton.edu |
| Pseudocode | Yes | Algorithm 1 OMLE-Equilibrium, Subroutine 1 Optimistic_Equilibrium(B), Algorithm 2 multi-step OMLE-Equilibrium, Algorithm 3 OMLE-Adversary |
| Open Source Code | No | The paper is purely theoretical and does not provide any statement or link regarding the release of source code. |
| Open Datasets | No | The paper is purely theoretical and does not use or describe any dataset, public or otherwise. |
| Dataset Splits | No | The paper is purely theoretical and does not describe any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is purely theoretical and does not mention any hardware specifications for running experiments. |
| Software Dependencies | No | The paper is purely theoretical and does not mention specific software dependencies with version numbers for running experiments. |
| Experiment Setup | No | The paper is purely theoretical and focuses on algorithm design and theoretical guarantees, thus it does not include details on experimental setup such as hyperparameters or system-level training settings. |