Reinforcement Learning in Robotic Hands

This chapter examines the role of reinforcement learning in controlling robotic hands, featuring OpenAI's innovative techniques for tasks like solving a Rubik's Cube. It contrasts the ease of training AI in controlled environments with the challenges faced in real-world applications, stressing the importance of experiential learning for robots. The discussion includes novel approaches like upside down reinforcement learning and highlights future possibilities in robotics through curiosity-driven methods and human imitation.