One of the most critical task in Deep Reinforcement Learning is to find a good set of training hyperparameters.
Optuna is a library that helps you to automate the search. In this Unit, we'll study a little bit of the theory behind automatic hyperparameter tuning. We'll then try to optimize the parameters manually and then see how to automate the search using Optuna.
The content below comes from Antonin's Raffin ICRA 2022 presentations, he's one of the founders of Stable-Baselines and RL-Baselines3-Zoo.
1️⃣ 📹 First, let study what's Automatic Hyperparameter Tuning. Don't forget to 👍 the video 🤗.
2️⃣👩💻 Then let's dive on the hands-on, where we'll then try to optimize the parameters manually and then see how to automate the search using Optuna.
3️⃣ Now that you've learned to use Optuna, why not going back to our Deep Q-Learning hands-on and implement Optuna to find the best training hyperparameters 👉
