Data for "Auditing Single-Agent Reinforcement Learning for EV Charging Assignment: A Protocol-Amended Comparison of Trained, Untrained, and Heuristic Policies"
Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Data for "Auditing Single-Agent Reinforcement Learning for EV Charging Assignment: A Protocol-Amended Comparison of Trained, Untrained, and Heuristic Policies" Raw seed-level data and campaign manifests for a benchmark of five agents (Random, Adaptive Heuristic, Q-Learning, DQN, Double DQN) on EV charging-station assignment, simulated on real Rabat and Tangier (Morocco) road networks in SUMO. Includes: campaign manifests with SHA-256 provenance, raw per-seed CSVs for the confirmatory trained/untrained diagnostic (three scenarios) and the legacy 210-run benchmark, and the JSON summaries behind the manuscript's result tables. Integrity verifiable via the included SHA-256 manifest. Preliminary, data-only deposit. Simulation event logs and trained model weights are not included in this version; available from the corresponding author on request.
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