Merge branch 'main' into Ofer

RunCode.md

@@ -38,10 +38,10 @@ pkg> activate experiments
 .
 
 
-* Currently due to SatelliteDynamics.jl dependency on [SOFA.jl](https://github.com/sisl/SOFA.jl), it only supports Unix systems (MacOS and Linux)
+* Currently due to SatelliteDynamics.jl dependency on [SOFA.jl](https://github.com/sisl/SOFA.jl), it only supports Unix systems (MacOS and Linux). In order to use SatelliteDynamics.jl on Windows systems, the Windows Subsystem for Linux ([WSL](https://learn.microsoft.com/en-us/windows/wsl/install)) with a [GCC](https://gcc.gnu.org/) installation is required.
 
 ### 4. Running a simulation
-```julia
+```bash
 julia --project=experiments experiments/demo.jl
 ```
 
@@ -50,19 +50,15 @@ A quick analysis of the results, stored in the hist DataFrame, can be performed
 Where `bmdp` is the belief-MDP model, `hist` is the simulation history, `modelList` is a vector of strings describing the different hypothesis models, and `dir2save` is an optional string describing the library to save results and plots.  
 The function returns the nominal/initial and the final/revised sensor tasking schedule, and prints the means of the cumulative reward, number of changes to the plan, steps to resolve the hypothesis, and observations to the object of interest taken before making a decision.
 
+
+### 5. Optional ILP Simulation
 For some intuition into how the integer linear program (ILP) solution performs, one may execute
+
 ```bash
 julia --project=experiments experiments/ilp-plan.jl
 ```
-from the base SDAPOMDPs.jl directory to generate an animation illustrating the sensor tasking strategy of 3 ground-based sensors for a 12-hour period.
-
-### Updating
-
-Always use the latest version. To get the latest version, run
-```
-pkg> update
-```
 
+from the base SDAPOMDPs.jl directory to generate an animation illustrating the sensor tasking strategy of 3 ground-based sensors for a 12-hour period.
 
 ## Additional Information/FAQs
 

_includes/research.md

@@ -8,15 +8,18 @@ The goal is to improve the ability to evaluate internal- and physical- state hyp
 
 
 **Technical Approach**
+
 The approach can be broken down into two main steps:
 1. Generating a base plan using integer linear programming
 2. Generating a refined MCTS plan accounting for the object of interest
 
 
-### 1) Integer Linear Program
+### 1. Integer Linear Program
 The base integer linear programming approach aims to judiciously allocate sensors to space objects in a manner where the severity of the worst-case scenario is minimized. 
 
 Formally, the ILP is given by
+<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+
 <!-- <script type="text/javascript" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script> -->
 $$
 \begin{aligned}
@@ -28,11 +31,11 @@ $$
 \end{aligned}
 $$
 
-Here $$X_{ijt}$$ is a binary 3-dimensional control variable representing whether or not observer $$j$$ observers object $$i$$ at time step $$t$$, and $$O_{ijt}$$ represents whether or not observer $$j$$ *is able to* observe object $$i$$ at time $$t$$.
+Here $$X_{ijt}$$ is a binary 3-dimensional control variable representing whether or not observer $$j$$ observes object $$i$$ at time step $$t$$, and $$O_{ijt}$$ represents whether or not observer $$j$$ *is able to* observe object $$i$$ at time $$t$$.
 
 For ground based-sensors, the ILP plan can be visualized as follows:
-![ILP-Plan](../assets/images/ilp-plan-600.gif)
+![ILP-Plan](assets/images/ilp-plan-600.gif)
 .
 
-### 2) Monte Carlo Tree Search
+### 2. Monte Carlo Tree Search
 Building on the ILP solution as a baseline, we assume the existence of an object of interest in the catalogue, for which we seek to resolve a specific hypothesis. This work focuses on determining the drag configuration for the object in question. To achieve this, we use Monte Carlo Tree Search (MCTS) applied to a belief Markov Decision Process (MDP). The goal of the MCTS solver is to minimize the entropy of the distribution over possible hypotheses while minimally disrupting the baseline catalogue maintenance plan.