This article presents a comparison of the learning stability and the performance of two intelligent systems – a Hierarchical Multi-Agent Deep Q- Network (Hierarchical MADQN) and a standard Multi-Agent Deep Q-Network (MADQN) system for cognitive skill assessment and enhancement. Both frameworks are based on Howard Gardner’s Theory of Multiple Intelligences and incorporate each type of intelligence as an independent agent that interacts with a changing environment to maximize the development of a user’s cognitive skills. While the traditional MADQN uses a linear agent-based decision-making process, the Hierarchical MADQN structure uses low-level agents for action execution and high-level agents for sub-goal selection. Results are displayed as visuals with cumulative rewards and findings conclude the instability of the hierarchical structure is and how learning progress is less consistent. In contrast, the standard MADQN system shows smoother and more consistent reward accumulation with higher convergence. The hierarchical system, even with its organized decision-making procedure, requires better coordination mechanisms and hyper parameter tuning to match the efficiency and reliability of the standard MADQN approach. This comparison shows how reinforcement learning frameworks can support personalized learning and improve cognitive skill development in dynamic learning environments.