A Quantum-Enhanced Framework for Human-Like Reinforcement Learning: ARDNS-P-Quantum with Piagetian Stages

Traditional reinforcement learning (RL) models like Deep Q-Networks (DQNs) often lack human-like capabilities such as multi-timescale memory, adaptive plasticity, and cognitive developmental stages. We introduce ARDNS-P-Quantum, an enhanced version of the Adaptive Reward-Driven Neural Simulator with Piagetian Developmental Stages (ARDNS-P), integrating quantum computing to improve action selection. Using a 2-qubit quantum circuit with RY rotations and 16 shots, ARDNS-P-Quantum leverages a dual-memory system and Piaget's developmental stages to achieve superior performance in a 10×10 grid-world. Over 20,000 episodes, ARDNS-P Quantum attains a 99.2% goal-reaching success rate (vs. 91.9% for ARDNS-P and 84.5% for DQN), reduces steps to goal to 33.3 (vs. 149.2 for ARDNS-P and 72.1 for DQN), and increases mean rewards to 9.1169 (vs. 9.12 for ARDNS-P and 3.2207 for DQN) in the last 100 episodes. The quantum circuit design, with fewer shots and optimized depth, enhances exploration efficiency. This framework bridges RL, neuroscience, developmental psychology, and quantum computing, offering a scalable approach for human-like learning in dynamic environments.