After fixing a bug in my last post, my PPO agent's performance improved, but it was still far from the baseline. I suspected the entropy bonus—the hyperparameter that controls exploration—was poorly tuned.
My old habit would've been to just blindly guess new values. For this blog, I'm committed to a more systematic approach. So, I decided to build a "visual diagnostic toolkit" to understand what different entropy levels actually look like in practice.
The post is a deep dive into the visual signatures of agents with too much and too little exploration, using things like entropy heatmaps and plots of action probabilities over time. One of the most interesting discoveries was how misleading average rewards can be for high-entropy agents and why looking at the variance is crucial.
I'm documenting my whole journey of learning RL research from scratch and would be happy to answer any questions about the JAX/Flax implementation or the visualization techniques.
Hi HN, author here.
After fixing a bug in my last post, my PPO agent's performance improved, but it was still far from the baseline. I suspected the entropy bonus—the hyperparameter that controls exploration—was poorly tuned.
My old habit would've been to just blindly guess new values. For this blog, I'm committed to a more systematic approach. So, I decided to build a "visual diagnostic toolkit" to understand what different entropy levels actually look like in practice.
The post is a deep dive into the visual signatures of agents with too much and too little exploration, using things like entropy heatmaps and plots of action probabilities over time. One of the most interesting discoveries was how misleading average rewards can be for high-entropy agents and why looking at the variance is crucial.
I'm documenting my whole journey of learning RL research from scratch and would be happy to answer any questions about the JAX/Flax implementation or the visualization techniques.