Challenges of Real-World Reinforcement Learning
It addresses the gap between theoretical RL advances and practical deployment, which is crucial for researchers and engineers aiming to productionize RL in various domains.
The paper identifies nine key challenges that hinder the application of reinforcement learning (RL) to real-world systems, such as unmet assumptions in practice, and proposes metrics and a testbed for evaluating solutions to these challenges.
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are often hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. We present a set of nine unique challenges that must be addressed to productionize RL to real world problems. For each of these challenges, we specify the exact meaning of the challenge, present some approaches from the literature, and specify some metrics for evaluating that challenge. An approach that addresses all nine challenges would be applicable to a large number of real world problems. We also present an example domain that has been modified to present these challenges as a testbed for practical RL research.