Learning Rationalizable Equilibria in Multiplayer Games
Authors: Yuanhao Wang, Dingwen Kong, Yu Bai, Chi Jin
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we first propose a simple yet sample-efficient algorithm for finding a rationalizable action profile in multi-player general-sum games under bandit feedback... We further develop algorithms with the first efficient guarantees for learning rationalizable Coarse Correlated Equilibria (CCE) and Correlated Equilibria (CE). |
| Researcher Affiliation | Collaboration | Yuanhao Wang 1, Dingwen Kong 2, Yu Bai3, Chi Jin1 1Princeton University, 2Peking University, 3Salesforce Research |
| Pseudocode | Yes | Algorithm 1 Iterative Best Response; Algorithm 2 Hedge for Rationalizable "u03B5"-CCE; Algorithm 3 Adaptive Hedge for Rationalizable "; Algorithm 4 Rationalizable "; |
| Open Source Code | No | The paper does not contain any explicit statements about making its source code publicly available or provide a link to a code repository for the described methods. |
| Open Datasets | No | The paper focuses on theoretical algorithm design and analysis (sample complexity, proofs) and does not describe experiments using specific, publicly available datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving dataset splits (training, validation, test) for model validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware (like GPU or CPU models, memory, or cloud instances) used for running experiments. |
| Software Dependencies | No | The paper focuses on theoretical algorithms and their mathematical properties. It does not specify any software dependencies with version numbers required to implement or run the proposed algorithms. |
| Experiment Setup | No | The paper describes theoretical algorithms and their parameters for analysis, but it does not detail an experimental setup with specific hyperparameters, training configurations, or system-level settings for empirical evaluation. |