Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hierarchical Decision Making In Electricity Grid Management
Authors: Gal Dalal, Elad Gilboa, Shie Mannor
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare our results to prevailing heuristics, and show the strength of our method. and In this section we show results of IAPI algorithm on the IEEE RTS-96 test system |
| Researcher Affiliation | Academia | Gal Dalal EMAIL Elad Gilboa EMAIL Shie Mannor EMAIL Technion, Israel |
| Pseudocode | Yes | Algorithm 1 IAPI Algorithm (followed by a structured algorithm block with Input, Output, steps). |
| Open Source Code | Yes | The code for the simulation environment is available at https://github.com/galdl/icml16_iapi. |
| Open Datasets | Yes | We use daily demand and wind profiles based on real historical records as published in (Pandzic et al., 2015). and In our simulation we use Nepisodes = 50 episodes, each with a 3 day horizon. |
| Dataset Splits | No | The paper does not explicitly describe a validation set or split for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The DA policies are evaluated in parallel, on a 200 cores cluster. This is a general description and lacks specific hardware details (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | In our simulation we use Nepisodes = 50 episodes, each with a 3 day horizon. and In each cross-entropy iteration we evaluate 200 DA policies (N = 200) and choose the top 20-th percentile for updating Pψ. The DA policies are evaluated in parallel, on a 200 cores cluster. For the TD(0) algorithm we use discounting with γ = 0.95. and Line failure probability pi is set to 5 10 4 for each line, and its time-fill-fix E = 5. |