How do I evaluate reinforcement learning models?

How do I evaluate reinforcement learning models?

Evaluating reinforcement learning models typically involves assessing their performance using metrics like cumulative reward, average reward per episode, and convergence speed. Would you like to know more about specific evaluation techniques?

Yes, what are some common techniques for evaluation?

Common techniques include using a validation set, running multiple episodes to gather statistics, and comparing against baseline models. You can also use visualizations to analyze the learning curve.

How can I visualize the performance of my model?

You can visualize performance using plots like reward over time, learning curves, or heatmaps for action-value functions. Libraries like Matplotlib or Seaborn in Python can help with this.

What tools can I use for reinforcement learning evaluation?

Popular tools include OpenAI Gym for environments, TensorBoard for visualization, and Ray Rllib for scalable reinforcement learning.