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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
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. |