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fix: fix link in table of contents #59

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59 changes: 27 additions & 32 deletions practicals/RL_2023_prac.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -182,6 +182,8 @@
"\n",
">>>>[Build your own DQN!](#scrollTo=DN4gEJXabSvA)\n",
"\n",
">>[Beyond Discrete Actions - Policy Gradient Methods (Homework)](#scrollTo=G--a37gzd214)\n",
"\n",
">>[Conclusion](#scrollTo=fV3YG7QOZD-B)\n",
"\n",
">>[Feedback](#scrollTo=o1ndpYE50BpG)\n",
Expand Down Expand Up @@ -219,11 +221,7 @@
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},
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Expand Down Expand Up @@ -853,11 +851,7 @@
"cell_type": "code",
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"outputs": [],
"source": [
Expand Down Expand Up @@ -891,11 +885,7 @@
"cell_type": "code",
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},
"outputs": [],
"source": [
Expand Down Expand Up @@ -958,11 +948,7 @@
"cell_type": "code",
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"source": [
Expand Down Expand Up @@ -1290,11 +1276,7 @@
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"source": [
Expand Down Expand Up @@ -1348,12 +1330,7 @@
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"source": [
Expand Down Expand Up @@ -3664,6 +3641,24 @@
"\"\"\" % data_url)"
]
},
{
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"source": [
"## Beyond Discrete Actions - Policy Gradient Methods (Homework)"
]
},
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},
"source": [
"We have only covered a very limited set of reinforcement learning methods. If you would like to learn about a whole other family of techniques (on which current state of the art methods are based) please have a look at the practical from 2022 [here](https://colab.research.google.com/github/deep-learning-indaba/indaba-pracs-2022/blob/main/practicals/introduction_to_reinforcement_learning.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {
Expand Down Expand Up @@ -3775,4 +3770,4 @@
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}
}