Learning to Steer Learners in Games
Authors: Yizhou Zhang, Yian Ma, Eric Mazumdar
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide numerical experiments to illustrate the effectiveness of the algorithms in Appendix F. F. Numerical Experiments F.1. Empirical Simulations for Section 6.1 F.2. Empirical Simulations for Section 6.2 |
| Researcher Affiliation | Academia | 1Department of Computing & Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA 2Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA 92093, USA. Correspondence to: Yizhou Zhang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Playing Against Ascending Learner ... Algorithm 2 test ... Algorithm 3 Binary Search ... Algorithm 4 Playing Against Mirror Descent ... Algorithm 5 Explore Row |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | No | The paper describes constructed game instances for its numerical experiments, such as 'Matching pennies' and 'Constructed game instance 1/2', rather than utilizing publicly available datasets. No links or citations to open datasets are provided. |
| Dataset Splits | No | The paper conducts numerical simulations using constructed game instances, rather than working with traditional datasets that would require explicit training/test/validation splits. |
| Hardware Specification | No | The paper discusses 'Numerical Experiments' and 'Empirical Simulations' but does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run these experiments. |
| Software Dependencies | No | The paper mentions methods like 'Online Gradient descent (OGD)' and 'Stochastic Mirror descent with KL regularizer', and refers to a step size 'ηt = η0/√t', but it does not specify any software libraries, frameworks, or their version numbers that were used for implementation. |
| Experiment Setup | Yes | For Binary Search, we set the accuracy margin d = 0.01. For each pure strategy of the optimizer, we set the number of steps for exploration to be k = 50. For all experiments in this section, we assume the learner is using Online Gradient descent (OGD) with step size ηt = η0/√t. For the purpose of properly displaying the interaction and learning process, we choose different η0 for different game instances. |