DQN

Introduction In the rapidly evolving world of artificial intelligence, reinforcement learning (RL) stands out as a powerful framework for training AI agents to make decisions in complex and dynamic environments. However, traditional RL algorithms often come with a significant drawback: they are highly specialized and require meticulous tuning for each specific task, making them less adaptable and more resource-intensive. Enter MR.Q (Model-based Representations for Q-learning)—a groundbreaking advancement in the field of reinforcement learning.