The Challenges of Real World Reinforcement Learning

Reinforcement Learning (RL) has had numerous successes in recent years in solving complex problem domains. However, this progress has been largely limited to domains where a simulator is available or the real environment is quick and easy to access. This is one of a number of challenges that are bottlenecks to deploying RL agents on real-world systems. Two recent papers identify nine important challenges that, if solved, will take a big step towards enabling RL agents to be deployed to real-world systems (Dulac et. al. 2019, 2020).The goals of this workshop are four-fold: (1) Providing a forum for researchers in academia, industry researchers as well as industry practitioners from diverse backgrounds to discuss the challenges faced in real-world systems; (2) discuss and prioritize the nine research challenges. This includes determining which challenges we should focus on next, whether any new challenges should be added to the list or existing ones removed from this list; (3) Discuss problem formulations for the various challenges and critique these formulations or develop new ones. This is especially important for more abstract challenges such as explainability. We should also be asking ourselves whether the current Markov Decision Process (MDP) formulation is sufficient for solving these problems or whether modifications need to be made. (4) Discuss approaches to solving combinations of these challenges.