How do you evaluate the performance and robustness of your reinforcement learning agent?

1 Define your objectives

Before you start evaluating your RL agent, you need to define your objectives and criteria for success. What are you trying to achieve with your agent? What are the desired outcomes and behaviors? How do you measure the quality and efficiency of your agent's actions? Depending on your problem and domain, you may have different objectives and metrics, such as reward, accuracy, speed, safety, or generalization.

2 Track your learning curves

One of the simplest and most common ways to evaluate your RL agent is to track its learning curves, which show how the agent's performance changes over time or episodes. Learning curves can help you monitor the agent's progress, identify potential problems, and compare different algorithms or parameters. For example, you can plot the cumulative reward, the average reward per episode, the number of steps per episode, or the success rate over time.

3 Test your agent on unseen scenarios

Another way to evaluate your RL agent is to test it on unseen or novel scenarios that are different from the ones it was trained on. This can help you assess the agent's generalization and adaptation abilities, as well as its robustness to uncertainty and noise. For example, you can change the environment dynamics, the reward function, the initial state, the goal state, or the agent's actions and observe how the agent responds.

4 Analyze your agent's behavior

Besides measuring your RL agent's performance with numerical metrics, you can also analyze its behavior with qualitative methods. This can help you understand how and why your agent acts the way it does, what are its strengths and weaknesses, and what are the underlying mechanisms and strategies. For example, you can visualize the agent's policy, value function, or Q-table, or use tools like saliency maps, attention maps, or influence diagrams to explain the agent's decisions.

5 Compare your agent with baselines

Another way to evaluate your RL agent is to compare it with baselines, which are standard or reference points that you can use to benchmark your agent's performance. Baselines can be other RL algorithms, human experts, or optimal solutions. Comparing your agent with baselines can help you determine how well your agent performs relative to others, and what are the trade-offs and advantages of your agent.

6 Conduct ablation studies

Finally, you can evaluate your RL agent by conducting ablation studies, which are experiments that involve removing or modifying some components or features of your agent and observing the impact on its performance. Ablation studies can help you identify the most important and influential factors that affect your agent's performance, and how they interact with each other. For example, you can change the network architecture, the learning rate, the exploration strategy, or the reward shaping of your agent and measure the difference.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?