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. MR.Q bridges the gap between model-based and model-free methods by achieving the high performance typically associated with model-based approaches while retaining the simplicity and efficiency of model-free algorithms. The secret behind MR.Q’s success lies in its ability to learn model-based representations without the need for costly trajectory simulations, setting a new standard for adaptability and efficiency in AI-driven decision-making.