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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits
Authors: Qinghua Liu, Yuanhao Wang, Chi Jin
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Along this direction, we present a new complete set of positive and negative results: When the policies of the opponents are revealed at the end of each episode, we propose new efficient algorithms achieving K-regret bounds... This is complemented with an exponential lower bound... When the policies of the opponents are not revealed, we prove a statistical hardness result... To summarize, we provide a complete set of results including both efficient algorithms and fundamental limits for no-regret learning in Markov games with adversarial opponents. |
| Researcher Affiliation | Academia | Qinghua Liu * 1 Yuanhao Wang * 1 Chi Jin 1 1Princeton University, New Jersey, USA. Correspondence to: Qinghua Liu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Optimistic Policy EXP3, Subroutine 1 Optimistic Policy Evaluation, Algorithm 2 Adaptive Optimistic Policy EXP3, Subroutine 2 Optimistic Best Response |
| Open Source Code | No | The paper does not provide any specific link or explicit statement about releasing the source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and mathematical proofs, therefore it does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithmic design and mathematical analysis, rather than detailing an empirical experimental setup with hyperparameters or training configurations. |