Making Real-World Reinforcement Learning Practical

Lecture by Sergey Levine about progress on real-world deep RL. Covers these papers: A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning: https://sites.google.com/berkeley.edu... Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion: https://sites.google.com/berkeley.edu... Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention: https://sites.google.com/view/mtrf REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation: https://sites.google.com/view/reboot-... FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing: https://sites.google.com/view/fastrlap Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions: https://qtransformer.github.io/ Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators: https://rl-at-scale.github.io/