Human-Centric Cooperative MARL for Reliable EV Charging Coordination in Smart Cities: A Controlled Multi-Seed Comparison of Centralised and Decentralised Training
Efficient allocation of electric vehicles (EVs) to charging stations in dense urban environments requires decision-making under partial observability and timevarying traffic demand. We present a controlled empirical comparison of two multiagent reinforcement learning paradigms for cooperative EV charging assignment in a SUMO-based urban network: Independent Q-Learning (IQL; DTDE) and QMIX (CTDE). Across 7 random seeds and 200 training episodes per seed (2,800 episodes in total), QMIX attains a mean training acceptance rate of 81.5±17.1%, versus 66.0 ± 16.8% for IQL. Under pure-greedy exploitation (ε=0, frozen weights, 32 episodes per seed), QMIX reaches 83.8±20.5% acceptance with 95.4% successper- accept, versus 64.4 ± 15.5% and 93.6% for IQL (Wilcoxon: T = 0, p ≤ 0.016; paired Cohen’s dz = 8.6). Non-RL baselines (NEAREST, WEIGHTED) accept all requests (100%) but incur higher timeout rates (9.3% and 4.1%), highlighting the value of learned selective acceptance. An ablation analysis suggests that, within this tabular-IQL vs. deep-QMIX comparison, CTDE/deep mixing— rather than reward shaping alone—better explains the gap.