Adaptive Multi-Objective Reinforcement Learning with Classical and Variational Quantum Deep Q-Networks

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

This paper presents an adaptive multi-objective reinforcement learning (MORL) framework integrating classical Deep Q-Networks (DQN) and Variational Quantum Circuit DQN (VQC-DQN).A modified CartPole environment provides two reward signals combined through an adaptive weighting mechanism that updates every 10 episodes and converges in 14 of 15 runs; a corrected replay buffer rescalarizes stored transitions with the current weight instead of a stale one.Across five weight configurations, classical DQN achieves the highest whole-training balance reward, while a parameter-matched Tiny-Classical baseline (37 parameters) attains the best normalized hypervolume (0.412 vs. 0.376 for VQC-DQN and 0.262 for classical); VQC-DQN outperforms Tiny-Classical on raw balance in 3 of 5 weight settings.Retraining VQC-DQN for 200 episodes instead of 150 increases near-convergence balance by 32-163%.At 200 episodes, VQC-DQN has higher point-estimate near-convergence balance than classical DQN across the tested weight settings; however, this comparison is suggestive rather than statistically established because of unequal seed counts, a single 20-episode evaluation window, and unavailable per-seed variance for this metric.A qubit-count sweep under a matched gradient-step budget finds no evidence of barren plateaus within the tested 2-10 qubit range (gradient norm increases, not decreases, with qubit count), while a Deep Sea Treasure benchmark shows classical DQN fails under extreme weights whereas VQC-DQN consistently reaches a treasure but converges to the shallowest policy.These results suggest that classical-versus-quantum comparisons are budget-dependent rather than absolute, pending variance-controlled, multi-seed confirmation.