wow thanks for submitting this and putting these links together. is there a better way to get up to speed on this than going through the papers in order and trying to replicate (in simulators) one at a time? Best way I can think of to try it is with Unity + Python, but there's a lot of rabbit hole risk there.
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation
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/