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Thomas Simonini
2022-12-19 12:44:34 +01:00
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notebooks/unit3/unit3.ipynb
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@@ -251,10 +251,7 @@
},
"source": [
"## Install dependencies 🔽\n",
"We can now install the dependencies RL-Baselines3 Zoo needs (this can take 5min ⏲)\n",
"\n",
"But we'll also install:\n",
"- `huggingface_sb3`: Additional code for Stable-baselines3 to load and upload models from the Hugging Face 🤗 Hub."
"We can now install the dependencies RL-Baselines3 Zoo needs (this can take 5min ⏲)"
]
},
{
@@ -279,18 +276,6 @@
"!pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RLRGKFR39l9s"
},
"outputs": [],
"source": [
"%%capture\n",
"!pip install huggingface_sb3"
]
},
{
"cell_type": "markdown",
"metadata": {
@@ -382,7 +367,7 @@
},
"source": [
"## Let's evaluate our agent 👀\n",
"- RL-Baselines3-Zoo provides `enjoy.py` to evaluate our agent.\n",
"- RL-Baselines3-Zoo provides `enjoy.py`, a python script to evaluate our agent. In most RL libraries, we call the evaluation script `enjoy.py`.\n",
"- Let's evaluate it for 5000 timesteps 🔥"
]
},
@@ -441,7 +426,7 @@
"- You can **showcase our work** 🔥\n",
"- You can **visualize your agent playing** 👀\n",
"- You can **share with the community an agent that others can use** 💾\n",
"- 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"
]
},
{
@@ -676,7 +661,7 @@
"## Some additional challenges 🏆\n",
"The best way to learn **is to try things by your own**!\n",
"\n",
"In the [Leaderboard](https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard) you will find your agents. Can you get to the top?\n",
"In the [Leaderboard](https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard) you will find your agents. Can you get to the top?\n",
"\n",
"Here's a list of environments you can try to train your agent with:\n",
"- BeamRiderNoFrameskip-v4\n",