Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium
Authors: Yuma Fujimoto, Kaito Ariu, Kenshi Abe
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Then, we prove their dynamics are identical. Furthermore, theoretically and experimentally, we clarify that the learning dynamics diverge from the Nash equilibrium in multi-memory zero-sum games and reach heteroclinic cycles (sojourn longer around the boundary of the strategy space), providing a fundamental advance in learning in games. |
| Researcher Affiliation | Collaboration | Yuma Fujimoto1,2,3 , Kaito Ariu3,4 and Kenshi Abe3 1Research Center for Integrative Evolutionary Science, SOKENDAI. 2Universal Biology Institute (UBI), the University of Tokyo. 3AI Lab, Cyber Agent, Inc. 4KTH Royal Institute of Technology. |
| Pseudocode | Yes | Algorithm 1 Discretized MMRD Input: η 1: for t = 0, 1, 2, do ... Algorithm 2 Discretized MMGA Input: η, γ 1: for t = 0, 1, 2, do ... |
| Open Source Code | Yes | The codes that we used are available at https://github.com/ Cyber Agent AILab/with-memory games |
| Open Datasets | No | The paper describes standard game theory setups (e.g., matching-pennies game, rock-paper-scissors game) which are defined within the paper, but it does not use or provide access information for a separate, publicly available dataset. |
| Dataset Splits | No | The paper describes simulations of game theory dynamics and does not refer to traditional training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments or simulations. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | The step-size is 10^-2 in common. ... We use Algorithm 2 with η = 10^-3 and γ = 10^-6. ... where we use Algorithm 2 with η = 10^-2 and γ = 10^-6. |