Reinforcement Learning Agent

Reinforcement learning (RL) agents are computational systems designed to learn optimal decision-making strategies through trial and error, aiming to maximize cumulative rewards within a defined environment. Current research emphasizes improving RL agent efficiency and robustness, focusing on areas like scalable architecture search, offline training with real-world and simulated data, and incorporating safety mechanisms and ethical considerations into agent design. These advancements are significant for diverse applications, including building energy optimization, personalized recommendations, and autonomous systems, driving progress in both theoretical understanding and practical deployment of RL.