Challenges of Real-World Reinforcement Learning

Understand

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.