Several further improvements in notebooks

aai-institute · Dec 13, 2023 · 59bf992 · 59bf992
1 parent e335a1c
commit 59bf992
Show file tree

Hide file tree

Showing 34 changed files with 5,038 additions and 1,118 deletions.
diff --git a/docs/conf.py b/docs/conf.py
@@ -141,7 +141,7 @@ def lineno_from_object_name(source_file, object_name):
 # built documents.
 #
 # The full version, including alpha/beta/rc tags.
-version = pkg_resources.get_distribution(project).version
+version = "0.1.0"
 release = version
 # The short X.Y version.
 major_v, minor_v = version.split(".")[:2]

diff --git a/notebooks/_config.yml b/notebooks/_config.yml
@@ -1,7 +1,7 @@
 # Book settings
 # Learn more at https://jupyterbook.org/customize/config.html
 
-title: thesan_output
+title: Reinforcement Learning and Control
 author: appliedAI TransferLab
 logo: _static/images/transferlab-logo.svg
 

diff --git a/notebooks/Images/offline_RL.jpg → notebooks/_static/images/offline_RL.jpg b/notebooks/Images/offline_RL.jpg → notebooks/_static/images/offline_RL.jpg
diff --git a/notebooks/Images/offline_RL_1.jpg → notebooks/_static/images/offline_RL_1.jpg b/notebooks/Images/offline_RL_1.jpg → notebooks/_static/images/offline_RL_1.jpg
diff --git a/notebooks/Images/offline_RL_2.jpg → notebooks/_static/images/offline_RL_2.jpg b/notebooks/Images/offline_RL_2.jpg → notebooks/_static/images/offline_RL_2.jpg
diff --git a/notebooks/Images/offline_RL_3.jpg → notebooks/_static/images/offline_RL_3.jpg b/notebooks/Images/offline_RL_3.jpg → notebooks/_static/images/offline_RL_3.jpg
diff --git a/...s/Images/policy_constraint_vs_support.png → ...c/images/policy_constraint_vs_support.png b/...s/Images/policy_constraint_vs_support.png → ...c/images/policy_constraint_vs_support.png
diff --git a/notebooks/Images/q_value_iterative.png → ...ooks/_static/images/q_value_iterative.png b/notebooks/Images/q_value_iterative.png → ...ooks/_static/images/q_value_iterative.png
diff --git a/notebooks/Images/stiching.png → notebooks/_static/images/stiching.png b/notebooks/Images/stiching.png → notebooks/_static/images/stiching.png
diff --git a/notebooks/nb_131_Minari_Overview.ipynb b/notebooks/nb_131_Minari_Overview.ipynb
diff --git a/notebooks/nb_150_Imitation_learning_theory.ipynb b/notebooks/nb_150_Imitation_learning_theory.ipynb
diff --git a/notebooks/nb_190_Final_Remarks.ipynb b/notebooks/nb_190_Final_Remarks.ipynb
diff --git a/notebooks/nb_20_IntroductionToControl.ipynb b/notebooks/nb_20_IntroductionToControl.ipynb
@@ -118,7 +118,7 @@
     }
    },
    "source": [
-    "# Introduction\n",
+    "# Introduction to Control\n",
     "\n",
     "Control theory is a field of control engineering and applied mathematics that deals with the control of dynamical systems in engineered processes and machines. The objective is to develop a model or algorithm governing the application of system inputs to drive the system to a desired state, while minimizing any delay, overshoot, or steady-state error and ensuring a level of control stability; often with the aim to achieve a degree of optimality. "
    ]
@@ -446,7 +446,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": false,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -3891,7 +3890,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": false,
     "slideshow": {
      "slide_type": "subslide"
     }

diff --git a/notebooks/nb_30_ControlAndPlanning.ipynb b/notebooks/nb_30_ControlAndPlanning.ipynb
@@ -126,7 +126,7 @@
     }
    },
    "source": [
-    "# Introduction\n",
+    "# Control and Planning\n",
     "\n",
     "In previous sections, we have designed feedback controllers for various systems with the goal of regulating the system output to a desired setpoint. Specifically, we utilized Fullstate Feedback and PID controllers. While these simple controllers can effectively regulate many systems, they have limitations that prevent high performance control for more complex systems.\n",
     "\n",
@@ -1542,7 +1542,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": false,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -2166,7 +2165,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": false,
     "slideshow": {
      "slide_type": "subslide"
     },
@@ -3234,7 +3232,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": false,
     "slideshow": {
      "slide_type": "subslide"
     },

diff --git a/notebooks/nb_40_RecentDevelopmentsInControl.ipynb b/notebooks/nb_40_RecentDevelopmentsInControl.ipynb
@@ -111,7 +111,7 @@
     }
    },
    "source": [
-    "# Introduction\n",
+    "# Recent Developments in Control Theory\n",
     "\n",
     "- So far we have focused on deterministic systems with no noise or disturbances.\n",
     "- In this part of the training, we will focus on stochastic systems and how MPC can be used to handle such systems.\n",
@@ -791,7 +791,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": false,
     "slideshow": {
      "slide_type": "subslide"
     }
@@ -949,7 +948,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": false,
     "slideshow": {
      "slide_type": "subslide"
     }