Offline Reinforcement Learning

Offline reinforcement learning (RL) is a re-emerging area of study that aims to learn behaviors using only logged data, such as data from previous experiments or human demonstrations, without further environment interaction. It has the potential to make tremendous progress in a number of real-world decision-making problems where active data collection is expensive (e.g., in robotics, drug discovery, dialogue generation, recommendation systems) or unsafe/dangerous (e.g., healthcare, autonomous driving, or education). Such a paradigm promises to resolve a key challenge to bringing reinforcement learning algorithms out of constrained lab settings to the real world. The first edition of the offline RL workshop, held at NeurIPS 2020, focused on and led to algorithmic development in offline RL. This year we propose to shift the focus from algorithm design to bridging the gap between offline RL research and real-world offline RL. Our aim is to create a space for discussion between researchers and practitioners on topics of importance for enabling offline RL methods in the real world. To that end, we have revised the topics and themes of the workshop, invited new speakers working on application-focused areas, and building on the lively panel discussion last year, we have invited the panelists from last year to participate in a retrospective panel on their changing perspectives.


For details on submission please visit: https://offline-rl-neurips.github.io/2021 (Submission deadline: October 6, Anywhere on Earth)

Speakers:
Aviv Tamar (Technion - Israel Inst. of Technology)
Angela Schoellig (University of Toronto)
Barbara Engelhardt (Princeton University)
Sham Kakade (University of Washington/Microsoft)
Minmin Chen (Google)
Philip S. Thomas (UMass Amherst)