A Novel Framework for Human-like Reinforcement Learning: ARDNS-P with Piagetian Stages

Human reinforcement learning (RL) integrates multi-timescale memory and adaptive learning strategies that evolve with cognitive development—features often absent in traditional RL models like Q-learning and Deep Q-Networks (DQNs). This paper introduces the Adaptive Reward-Driven Neural Simulator with Piagetian Developmental Stages (ARDNS-P), a novel framework combining neuroscience-inspired mechanisms with Jean Piaget's theory of cognitive development. ARDNS-P employs a dual-memory system for short- and long-term contextualization, a variance-modulated plasticity rule, and a developmental progression inspired by Piaget's stages (sensorimotor, preoperational, concrete operational, and formal operational). We evaluate ARDNS-P against a DQN baseline in a dynamic 10x10 grid-world environment over 20000 episodes. ARDNS-P achieves a 91.9% goal-reaching success rate (18381/20000 episodes) compared to DQN's 83.4% (16675/20000), with greater efficiency in steps to goal (mean 149.2 vs. 178.5 in the last 50 episodes) and higher cumulative rewards (estimated 9.12 vs. 8.24 in the last 50 episodes). ARDNS-P demonstrates strong potential for human-like learning in cognitive AI, robotics, and neuroscience-inspired systems, with opportunities for further optimization to reduce reward variability.