RL Agent

Reinforcement learning (RL) agents are computational entities designed to learn optimal decision-making strategies through trial and error within a defined environment. Current research emphasizes improving data efficiency, particularly through techniques like curriculum learning, action masking, and the integration of quantum computing methods. These advancements are crucial for deploying RL agents in complex real-world scenarios, such as cybersecurity, robotics, and healthcare, where data scarcity or safety concerns are paramount. Furthermore, significant effort is dedicated to enhancing the explainability and robustness of RL agents, addressing challenges like negative transfer and the impact of reward misspecification.