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

Umberto Gonçalves de Sousa

Published Apr 18, 2025• 2 citations
Publisher

Abstract

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.

DOI
10.20944/preprints202504.1523.v1

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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.
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This paper presents Q-ARDNS-Multi, an advanced multi-agent quantum reinforcement learning (QRL) framework that extends the ARDNS-FN-Quantum model, where Q-ARDNS-Multi stands for "Quantum Adaptive Reward-Driven Neural Simulator - Multi-Agent". It integrates quantum circuits with RY gates, meta-cognitive adaptation, and multi-agent coordination mechanisms for complex 3D environments. Q-ARDNS-Multi leverages a 2-qubit quantum circuit for action selection, a dual-memory system inspired by human cognition, a shared memory module for agent cooperation, and adaptive exploration strategies modulated by reward variance and intrinsic motivation. Evaluated in a 10×10×310 \times 10 \times 310×10×3 GridWorld environment with two agents over 5000 episodes, Q-ARDNS-Multi achieves success rates of 99.6\% and 99.5\% for Agents 0 and 1, respectively, outperforming Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Soft Actor-Critic (SAC) in terms of success rate, stability, navigation efficiency, and collision avoidance. The framework records mean rewards of −304.2891±756.4636-304.2891 \pm 756.4636−304.2891±756.4636 and −295.7622±752.7103-295.7622 \pm 752.7103−295.7622±752.7103, averaging 210 steps to goal, demonstrating its robustness in dynamic settings. Comprehensive analyses, including learning curves, reward distributions, statistical tests, and computational efficiency evaluations, highlight the contributions of quantum circuits and meta-cognitive adaptation. By bridging quantum computing, cognitive science, and multi-agent RL, Q-ARDNS-Multi offers a scalable, human-like approach for applications in robotics, autonomous navigation, and decision-making under uncertainty.
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