Continuous Control Task

Continuous control tasks involve training agents to perform actions smoothly and continuously in dynamic environments, aiming to optimize performance metrics like reward maximization. Current research emphasizes robust and safe learning, focusing on techniques like model-based reinforcement learning (MBRL), hierarchical reinforcement learning (HRL), and ensemble methods to improve sample efficiency, generalization, and safety in complex scenarios. These advancements are crucial for deploying reinforcement learning in real-world applications, particularly in robotics, where safe and efficient control is paramount.