OFFLINE

Alternatively, Offline RL focuses on training agents with logged data with no further environment interaction. Offline RL promises to bring forward a data-driven RL paradigm and carries the potential to scale up end-to-end learning approaches to real-world decision making tasks such as robotics, recommendation systems, dialogue generation, autonomous driving, healthcare systems and safety-critical applications. Recently, successful deep RL algorithms have been adapted to the offline RL setting and demonstrated a potential for success in a number of domains, however, significant algorithmic and practical challenges remain to be addressed. Within the past two years, performance on simple benchmarks has rapidly risen, so the community has also started developing standardized benchmarks (RLUnplugged, D4RL) specifically designed to stress-test offline RL algorithms... Goal of the workshop: Our goal is to bring attention to offline RL, both from within and from outside the RL community..