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 and you’ve heard about at least some of the most common RL algorithms and environments.
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. I’ve tried to not depend too much on readers’ prior knowledge and where I couldn’t omit some details, I’ve put references to useful materials.
Figure 1: The Reinforcement Learning framework (Sutton & Barto, 2018).
I’ll start by discussing useful metrics that give us a glimpse into the training and decision processes of the agent.
Then we will focus on the aggregation statistics of these metrics, like average, that will help us analyze them for many episodes played by the agent throughout the training. These will help root cause any issues with the agent.
At each step, I’ll base my suggestions on my own experience in RL research. Let’s jump right into it!
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