merge

README.md

@@ -7,22 +7,41 @@ The framework is designed to be modular and can be easily extended to support
 other types of neural networks and other types of partitioning algorithms.
 
 ## Installation
+
 The framework is built on top of PyTorch, so you need to install PyTorch first.
 then you can install the framework using pip:
+
 ```
 pip install -r requirements.txt
 ```
 
 ## Usage
+
 Run all the cases using the following command:
+
 ```
 cd DynPartition
 export PYTHONPATH="${pwd}"
 sh dynPartition/run.sh
 ```
-The results will be saved in the `dynPartition/_plots` directory.
+
+The results will be saved in the `dynPartition/_logs`
+directories.
 
 ## Results
-The detailed analysis of this framework is presented in the "Project Report.pdf"
+
+The results can be created by using scripts in the `dynPartition/graphs`
+directory.
+
+## Report
+
+The detailed analysis of this framework is presented in the `Project Report.pdf`
 file.
 
+## Branches
+
+The framework is implemented in two branches:
+
+- `master`: uses Reinforcement Learning scheduler based on Deep-Q Learning.
+- `using_policy_value`: uses Reinforcement Learning scheduler based on Policy
+  Gradient.