feat: setting up github actions to run on every push

.github/workflows/main.yml

@@ -1,8 +1,6 @@
 name: Release Senior Thesis
-on:
-  push:
-    tags:
-      - '*.*.*'
+on: [push]
+
 jobs:
   publish:
     runs-on: ubuntu-latest
@@ -37,6 +35,7 @@ jobs:
       - name: Create release
         id: create_release
         uses: actions/create-release@v1
+        if: startsWith(github.ref, 'refs/tags/')
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         with:
@@ -47,6 +46,7 @@ jobs:
       - name: Upload released asset
         id: upload-release-asset
         uses: actions/upload-release-asset@v1
+        if: startsWith(github.ref, 'refs/tags/')
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         with:

abstract.md

@@ -5,15 +5,15 @@ surroundings in a real world environment, and it is necessary to realize
 technologies such as fully autonomous unmanned aerial vehicles (UAVs) and land
 vehicles. Reinforcement Learning (RL) has proven to be a novel and effective
 method for autonomous navigation and control, as it is capable of optimizing a
-method of converting its instantaneous state to an action at a point in time
-[@gugan2023; @song2023; @doukhi2022]. Here we use a Deep Deterministic Policy
-Gradient (DDPG) RL algorithm to train the COEX Clover quadcopter system to
-perform autonomous navigation. With the advent of solid state lasers,
-miniaturized optical ranging systems have become ubiquitous for aerial robotics
-because of their low power and accuracy [@raj2020]. By equipping the Clover with
-ten Time of Flight (ToF) ranging sensors, we supply continuous spatial data in
-combination with inertial data to determine the quadcopter's state, which is
-then mapped to its control output. Our results suggest that, while the DDPG
-algorithm is capable of training a quadcopter system for autonomous navigation,
-its computation-heavy nature leads to delayed convergence, and relying on
-discretized algorithms may permit more rapid convergence across episodes.
+method of converting its instantaneous state to an action at a point in time.
+Here we use a Deep Deterministic Policy Gradient (DDPG) RL algorithm to train
+the COEX Clover quadcopter system to perform autonomous navigation. With the
+advent of solid state lasers, miniaturized optical ranging systems have become
+ubiquitous for aerial robotics because of their low power and accuracy. By
+equipping the Clover with ten Time of Flight (ToF) ranging sensors, we supply
+continuous spatial data in combination with inertial data to determine the
+quadcopter's state, which is then mapped to its control output. Our results
+suggest that, while the DDPG algorithm is capable of training a quadcopter
+system for autonomous navigation, its computation-heavy nature leads to delayed
+convergence, and relying on discretized algorithms may permit more rapid
+convergence across episodes.

thesis.md

@@ -94,6 +94,8 @@ using simpler, more economically affordable sensors can enable a quadcopter to
 fly in a GPS-denied environment without the use of LiDAR, which is typically an
 order of magnitude more expensive.
 
+<!-- FIXME: rewrite in past tense -->
+
 ## Ethical Implications
 
 ### Civilian Use
@@ -424,7 +426,7 @@ a case for the expected adaptability of a DDPG algorithm in curriculum learning.
 Because both PPO and DDPG are model-free algorithms with continuous state and
 action spaces, we expect similar levels of aptness for curriculum learning.
 
-# Method of approach
+# Method of Approach
 
 This project uses the Copter Express (COEX) Clover quadcopter platform, equipped
 with Time of Flight (ToF) ranging sensors, and applies a Deep Deterministic
@@ -944,6 +946,10 @@ the number of episodes increases.
 
 ## Theory
 
+<!--
+FIXME: restructure by breaking into "algorithmic" and "sensor" subsections
+-->
+
 ### Deep Reinforcement Learning
 
 As stated, this project uses a Deep RL algorithm known as the Deep Deterministic
@@ -1647,6 +1653,10 @@ order of days or weeks.
 
 ![Episodic duration versus episode number for run 1, whose training results are displayed in {+@fig:plot1}.](images/plots/plot-episode-duration.png){#fig:plot-episode-duration width=100%}
 
+### Threats to Validity
+
+<!-- FIXME: add threats to validity section -->
+
 # Future Work
 
 The results of this project suggest the need for more extensive training using