This chapter aims at proposing a detailed model based on deep reinforcement learning as a method for effective evaluation and enhancing cognitive skills. In the proposed model, the adaptation of the elements of both the content and the user interface according to the individual needs of the learners’ current level of personal knowledge is a key feature. Gardner’s theory of the existence of multiple intelligences is used as a working hypothesis for the classification of human cognitive skills. The main framework for identifying the most distinctive types of personal intelligence is a Deep Q network. A multi-agent Deep Q network (MADQN) is utilized as each agent is conceptualized as an autonomous entity, embodying a distinct intelligence type with a unique set of rules and data. Thus, the architecture is composed of eight agents that interact with the environment (the user’s results) as well as with each other in real time. MADQN teaches agents how to coordinate with each other and ensures optimal actions in an efficient way. The multi-agent network addresses the challenge of systematization and real-time refinement of cognitive skills. The implementation of the model and its scalable solutions are subject to further development.