Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions
Authors: Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, Abhishek Sethi5399-5406
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments corroborate our theoretical findings and also find a similar convergence when we use other strategies such as Deep Q-Learning. We complement these results by simulating the above model with experiments. In particular, we show that these algorithms converge and produce truthful (or the canonical) equilibria. Furthermore, we show that the algorithm converges much quicker than the theory would predict. |
| Researcher Affiliation | Collaboration | Zhe Feng,1 Guru Guruganesh, 2 Christopher Liaw, 3 Aranyak Mehta, 2 Abhishek Sethi 2 1 Harvard University 2 Google Research 3 University of British Columbia |
| Pseudocode | Yes | Algorithm 1 Mean-based (Contextual) Learning Algorithm of Bidder i |
| Open Source Code | No | No explicit statement about providing open-source code for the described methodology or a link to a code repository was found. |
| Open Datasets | No | The paper describes generating data by sampling independent uniform distributions but does not refer to any specific publicly available dataset or provide access information for their generated data. |
| Dataset Splits | No | The paper describes simulated environments but does not provide specific training/validation/test dataset split information. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned in the provided text. |
| Software Dependencies | No | No specific ancillary software details with version numbers (e.g., library names like PyTorch 1.9, or specific solvers like CPLEX 12.4) were mentioned. |
| Experiment Setup | Yes | The details of Deep Q-Learning model and the set of hyperparameters used to train the two Q models are outlined in Appendix C. |