Continual Reinforcement Learning Workshop

Reinforcement learning (RL) is traditionally studied under the assumption of stationarity. Yet, real-world systems operate in open-ended, evolving environments. This motivates continual reinforcement learning (CRL), where agents must learn, adapt, and retain knowledge under persistent non-stationarity. 

The full-day CRL workshop aims to consolidate perspectives across CRL and related areas, clarify foundational formulations, advance principled approaches, and discuss emerging applications. See our Call for Papers - topic of interest for details.

Submit Your Paper Now!

We are also looking for reviewers. If you are interested in reviewing papers, feel free to nominate yourself through this form!