Uc Berkeley Researchers Introduce Serl: A Software Suite For Sample-efficient Robotic Reinforcement Learning

Researchers have developed a library for robotic reinforcement learning that includes a sample-efficient off-policy deep RL method, tools for reward computation and environment resetting, and a high-quality controller for a widely adopted robot. This resource aims to address the challenge of implementing and utilizing robotic RL methods, which has hindered their widespread adoption and further development.