Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 16: RL for Robots

This lecture discusses autonomous learning, particularly in the context of reinforcement learning for robots, focusing on robotics use cases. It addresses why robots aren't already autonomous, defines the problem of autonomous reinforcement learning, and explores algorithms for learning policies without human intervention, including a formulation called single-life reinforcement learning. The lecture covers the challenges of applying traditional reinforcement learning to physical robots, where human intervention is often needed for resetting the environment. It introduces concepts like forward-backward RL and discusses different evaluation methods for autonomous RL systems, such as deployed policy evaluation and continuing policy evaluation. The lecture also touches on learning reset policies, task cycles, and adapting to new circumstances during deployment, emphasizing the importance of minimizing human supervision in robot training and operation.