Advancements in Deep Robotic Learning

This chapter delves into ongoing research in deep robotic learning, focusing on machines that continuously learn from real-world experiences. It explores the integration of reinforcement learning with traditional planning methods, emphasizing higher-level abstractions and task decomposition for improved efficiency. Additionally, the chapter highlights innovative approaches to memory utilization and goal representation, showcasing two research studies that enhance decision-making in machine learning.