Deep reinforcement learning (DRL) is widely assumed to outperform simpler rule-based and tabular baselines for sequential decision problems. We test this assumption for electric vehicle (EV) charging assignment using Simulation of Urban MObility (SUMO) simulations of real Rabat and Tangier road networks, with a protoco...
Version 2 of the data deposit for the manuscript "Auditing Single-Agent Reinforcement Learning for EV Charging Assignment: A Protocol-Amended Comparison of Trained, Untrained, and Heuristic Policies" (Algorithms, MDPI, manuscript ID algorithms-4572374). Version 2 replaces the preliminary data-only content of version 1...
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 assig...
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 assig...
