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Merge pull request #16 from aai-institute/adapt-logos
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Adapt logos to new ci
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@turnmanh
turnmanh committed Mar 24, 2024
2 parents 11f0b5f + 001e2a8 commit 320b1c9
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3 changes: 3 additions & 0 deletions notebooks/_static/images/aai-institute-cover.png
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4 changes: 2 additions & 2 deletions notebooks/_static/images/aai-logo.png
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4 changes: 2 additions & 2 deletions notebooks/_static/images/transferlab-logo-dark.svg
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4 changes: 2 additions & 2 deletions notebooks/_static/images/transferlab-logo.svg
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4 changes: 2 additions & 2 deletions notebooks/nb_20_IntroductionToControl.ipynb
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}
},
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Introduction to Control Theory</div>"
]
},
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]
},
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Thank you for your attention!</div>"
]
},
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4 changes: 2 additions & 2 deletions notebooks/nb_30_ControlAndPlanning.ipynb
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}
},
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"presentation first slide\" style=\"width:100%;\">\n",
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"presentation first slide\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Control and Planning</div>"
]
},
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]
},
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Thank you for the attention!</div>"
]
},
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4 changes: 2 additions & 2 deletions notebooks/nb_40_RecentDevelopmentsInControl.ipynb
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}
},
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Recent Developments in Control Theory</div>"
]
},
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]
},
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Thank you for the attention!</div>"
]
},
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4 changes: 2 additions & 2 deletions notebooks/nb_50_IntroRL.ipynb
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Expand Up @@ -307,7 +307,7 @@
}
},
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Intro to Reinforcement Learning</div>"
]
},
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]
},
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Thank you for the attention!</div>"
]
},
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4 changes: 2 additions & 2 deletions notebooks/nb_70_TrainingRLAgents.ipynb
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}
},
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Training RL Agents </div>"
]
},
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]
},
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Thank you for the attention!</div>"
]
}
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42 changes: 21 additions & 21 deletions notebooks/nb_75_EnvironmentEngineering.ipynb
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{
"cell_type": "code",
"execution_count": null,
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"outputs": [],
"source": [
"%%capture\n",
Expand All @@ -11,14 +14,14 @@
"%autoreload 2\n",
"%matplotlib inline\n",
"%load_ext training_rl"
],
"metadata": {
"collapsed": false
}
]
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{
"data": {
Expand Down Expand Up @@ -192,27 +195,27 @@
],
"source": [
"%presentation_style"
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"collapsed": false
}
]
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"cell_type": "code",
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"source": [
"%%capture\n",
"\n",
"%set_random_seed 12"
],
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{
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Expand All @@ -239,20 +242,17 @@
],
"source": [
"%load_latex_macros"
],
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"collapsed": false
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"cell_type": "markdown",
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Environments and Feature Engineering </div>"
],
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"collapsed": false
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"source": [
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\">Environments and Feature Engineering </div>"
]
},
{
"cell_type": "markdown",
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66 changes: 33 additions & 33 deletions notebooks/nb_90_IntroOfflineRL.ipynb
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{
"cell_type": "code",
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"metadata": {
"ExecuteTime": {
"end_time": "2023-12-16T17:57:00.726151Z",
"start_time": "2023-12-16T17:56:59.841417Z"
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"source": [
"%%capture\n",
Expand All @@ -11,61 +18,54 @@
"%matplotlib inline\n",
"%load_ext training_rl\n",
"%set_random_seed 12"
],
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"start_time": "2023-12-16T17:56:59.841417Z",
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"start_time": "2023-12-16T17:57:02.213509Z"
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"%presentation_style"
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"start_time": "2023-12-16T17:57:02.213509Z",
"end_time": "2023-12-16T17:57:02.243879Z"
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"%load_latex_macros"
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\"> Introduction to Offline Reinforcement Learning</div>"
]
},
{
"cell_type": "markdown",
"source": [
"# Introduction to Offline Reinforcement Learning"
],
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"collapsed": false
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},
"source": [
"# Introduction to Offline Reinforcement Learning"
]
},
{
"cell_type": "markdown",
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},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"## Benchmarking\n",
"\n",
"In most cases, offline RL methods typically train a policy with specific hyperparameters for a fixed number of steps. They then assess their quality by using the policy from the last iteration in an online evaluation. We will follow this approach in this workshop as it will allow us to focus on understanding concepts rather than fine-tuning optimization.\n",
"\n",
"However, it's worth noting that alternative methods in the realm of offline RL, known as **Offline Policy Evaluation (OPE)**, exist. These approaches aim to evaluate policy performance directly from the data without any interaction with the environment, which can be a realistic scenario. Although we won't delve into these methods in detail, it's important to acknowledge their existence. **It's worth noting that these methods are still considered somewhat unreliable, and there is ongoing progress in this area**."
],
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"cell_type": "markdown",
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34 changes: 17 additions & 17 deletions notebooks/nb_91_RLOpenSourceDatasets.ipynb
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{
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"source": [
"%%capture\n",
Expand All @@ -11,42 +14,39 @@
"%matplotlib inline\n",
"%load_ext training_rl\n",
"%set_random_seed 12"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "code",
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"%presentation_style"
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}
]
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{
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"source": [
"%load_latex_macros"
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]
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{
"cell_type": "markdown",
"source": [
"<img src=\"_static/images/aai-institute-cover.svg\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\"> Offline-RL open source datasets </div>"
],
"metadata": {
"collapsed": false
}
},
"source": [
"<img src=\"_static/images/aai-institute-cover.png\" alt=\"Snow\" style=\"width:100%;\">\n",
"<div class=\"md-slide title\"> Offline-RL open source datasets </div>"
]
},
{
"cell_type": "markdown",
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