Optimizing Prosumer Policies in Periodic Double Auctions Inspired by Equilibrium Analysis
Authors: Bharat Manvi, Sanjay Chandlekar, Easwar Subramanian
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The efficacy of the proposed algorithm is demonstrated on the Power TAC wholesale market simulator against several baselines and state-of-the-art bidding policies. |
| Researcher Affiliation | Collaboration | Bharat Manvi1 , Sanjay Chandlekar1,2 and Easwar Subramanian1 1TCS Research 2IIIT Hyderabad {bharat.manvi, easwar.subramanian}@tcs.com, sanjay.chandlekar@research.iiit.ac.in |
| Pseudocode | Yes | Algorithm 1 MPNE-BBS |
| Open Source Code | No | The paper does not provide an explicit statement or link to the authors' own open-source code for the methodology described. It mentions using the Power TAC simulator, which is a third-party platform. |
| Open Datasets | Yes | The numerical experiments are conducted on the wholesale market module of the Power Trading Agent Competition (Power TAC) [Ketter et al., 2020]. |
| Dataset Splits | No | The paper describes using the Power TAC simulator for experiments, but it does not specify explicit training, validation, or test dataset splits in terms of percentages, sample counts, or citations to predefined splits of a dataset. It mentions running multiple 'games' or 'simulations'. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions the Power TAC simulator but does not provide specific version numbers for any ancillary software, libraries, or programming languages used in the implementation. |
| Experiment Setup | No | While the paper describes the experimental environment (Power TAC) and certain aspects like number of games and demand configurations, it does not provide concrete numerical values for all hyperparameters or system-level training settings needed for full reproducibility, such as the specific values for πΌπ, π½π, πΌπ, π½π, and P mentioned in Algorithm 1. |