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https://github.com/huggingface/deep-rl-class.git
synced 2026-04-13 18:00:45 +08:00
Adding warning colab
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@@ -3,8 +3,8 @@
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "view-in-github"
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit3/unit3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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@@ -279,7 +279,9 @@
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"- We use the Atari Wrapper that preprocess the input (Frame reduction ,grayscale, stack 4 frames)\n",
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"- We use `CnnPolicy`, since we use Convolutional layers to process the frames\n",
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"- We train it for 10 million `n_timesteps` \n",
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"- Memory (Experience Replay) size is 100000"
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"- Memory (Experience Replay) size is 100000\n",
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"\n",
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"💡 My advice is to **reduce the training timesteps to 1M,** if you want to train for 10M timesteps, you should run on your local machine (to avoid Colab timeout) to do that just click on: `File>Download`"
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]
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},
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{
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@@ -712,12 +714,11 @@
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],
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"metadata": {
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"colab": {
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"authorship_tag": "ABX9TyPwiHKn+ccCskGi3ZMw9yH2",
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"collapsed_sections": [],
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"include_colab_link": true,
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"name": "Copie de Unit 3: Deep Q-Learning with Space Invaders.ipynb",
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"private_outputs": true,
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"provenance": []
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"provenance": [],
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"include_colab_link": true
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},
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"kernelspec": {
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"display_name": "Python 3",
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@@ -729,4 +730,4 @@
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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}
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