Unit 1: Bonus 🎁
- Our teammate @Chris Emezue published a new leaderboard where you can compare your trained agents in new environments 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
Try new environments 🎮
Now that you've played with LunarLander-v2 Why not try these environments? 🔥:
- 🗻 MountainCar-v0 https://www.gymlibrary.dev/environments/classic_control/mountain_car/
- 🏎️ CarRacing-v1 https://www.gymlibrary.dev/environments/box2d/car_racing/
- 🥶 FrozenLake-v1 https://www.gymlibrary.dev/environments/toy_text/frozen_lake/
A piece of advice 🧐
The first Unit, is a very interesting one but also a very complex one because it's where you learn the fundamentals.
That’s normal if you still feel confused with all these elements. This was the same for me and for all people who studied RL.
Take time to really grasp the material before continuing. It’s important to master these elements and having a solid foundations before entering the fun part.
We published additional readings in the syllabus if you want to go deeper 👉 https://github.com/huggingface/deep-rl-class/blob/main/unit1/README.md
The hands-on for the first Unit are more funny experiments, but as we'll go deeper, **you'll understand better how to choose the hyperparameters and what model to use. For now, have fun, try stuff you can't break the simulations 🚀 **