How to Make Sense of the Reinforcement Learning Agents? - KDnuggets

Based on simply watching how an agent acts in the environment it is hard to tell anything about why it behaves this way and how it works internally. That's why it is crucial to establish metrics that tell WHY the agent performs in a certain way. This is challenging especially when the agent doesn't behave the way we would like it to behave, … which is like always. Every AI practitioner knows that whatever we work on, most of the time it won't simply work out of the box (they wouldn't pay us so much for it otherwise). In this blog post, you'll learn what to keep track of to inspect/debug your agent learning trajectory. I'll assume you are already familiar with the Reinforcement Learning (RL) agent-environment setting (see Figure 1) and you've heard about at least some of the most common RL algorithms and environments. Nevertheless, don't worry if you are just beginning your journey with RL.