Offline Reinforcement Learning

Offline Reinforcement Learning describes training an agent without interacting with the environment. The agent learns from previously collected experiences such as from another RL policy trained online or from a human demonstrator. This video explores two recent advancements in Offline RL! Thanks for watching! Please Subscribe! Paper Links: An Optimistic Perspective on Offline Reinforcement Learning: https://ift.tt/3cjwheT Datasets for Data-Driven Reinforcement Learning: https://ift.tt/2xBQ2Qt Q-Learning (Wikipedia): https://ift.tt/1Bfm6QW AVID (Robot in intro animation): https://ift.tt/38A49TH Nature-inspired robotics (Robot in intro animation): https://ift.tt/34agMD6