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@@ -122,9 +122,6 @@ git clone https://github.com/DLR-RM/rl-baselines3-zoo
## Install dependencies 🔽
We can now install the dependencies RL-Baselines3 Zoo needs (this can take 5min ⏲)
But we'll also install:
- `huggingface_sb3`: Additional code for Stable-baselines3 to load and upload models from the Hugging Face 🤗 Hub.
```bash
cd /content/rl-baselines3-zoo/
```
@@ -133,10 +130,6 @@ cd /content/rl-baselines3-zoo/
pip install -r requirements.txt
```
```bash
pip install huggingface_sb3
```
## Train our Deep Q-Learning Agent to Play Space Invaders 👾
To train an agent with RL-Baselines3-Zoo, we just need to do two things:
@@ -175,7 +168,7 @@ python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Let's evaluate our agent 👀
- RL-Baselines3-Zoo provides `enjoy.py` to evaluate our agent.
- RL-Baselines3-Zoo provides `enjoy.py`, a python script to evaluate our agent. In most RL libraries, we call the evaluation script `enjoy.py`.
- Let's evaluate it for 5000 timesteps 🔥
```bash
@@ -199,7 +192,7 @@ This way:
- You can **showcase our work** 🔥
- You can **visualize your agent playing** 👀
- You can **share with the community an agent that others can use** 💾
- You can **access a leaderboard 🏆 to see how well your agent is performing compared to your classmates** 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
- You can **access a leaderboard 🏆 to see how well your agent is performing compared to your classmates** 👉 https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
To be able to share your model with the community, there are three more steps to follow:
@@ -293,7 +286,7 @@ But finding hyperparameters can be a daunting task. Fortunately, we'll see in th
The best way to learn **is to try things by your own**!
In the [Leaderboard](https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard) you will find your agents. Can you get to the top?
In the [Leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) you will find your agents. Can you get to the top?
Here's a list of environments you can try to train your agent with:
- BeamRiderNoFrameskip-v4