Merge pull request #14 from thuang/dev_src

update to V1.0.0_rc. after InterPSS major update
RLGC-Project · Dec 16, 2020 · 5aba238 · 5aba238
2 parents b7d4ad2 + c6df830
commit 5aba238
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Showing 25 changed files with 48 additions and 382 deletions.
diff --git a/.classpath b/.classpath
@@ -1,6 +1,5 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <classpath>
-	<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER/org.eclipse.jdt.internal.debug.ui.launcher.StandardVMType/JavaSE-1.8"/>
 	<classpathentry kind="src" path="src"/>
 	<classpathentry kind="lib" path="/ipss.lib.3rdPty/lib/apache/commons-logging.jar"/>
 	<classpathentry kind="lib" path="/ipss.lib.3rdPty/lib/apache/commons-math3-3.6.1.jar"/>
@@ -23,5 +22,6 @@
 	<classpathentry combineaccessrules="false" kind="src" path="/ieee.odm.schema"/>
 	<classpathentry kind="lib" path="/ipss.lib.3rdPty/lib/python/py4j-0.10.7.jar"/>
 	<classpathentry kind="lib" path="/ipss.lib.3rdPty/lib/json/gson-2.8.6.jar"/>
+	<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
 	<classpathentry kind="output" path="bin"/>
 </classpath>
diff --git a/README.md b/README.md
@@ -22,7 +22,7 @@ In this project, we explore to use deep reinforcement learning methods for contr
 
      ```
      cd RLGC
-     conda env create -f environment. yml
+     conda env create -f environment.yml
      ``` 
      
      or you can create your own environment. The main dependent modules/libs include gym, tensorflow, py4j, numpy, matplotlib, stable-baselines,jupyter-notebooks
@@ -62,14 +62,11 @@ grid environment definition version 5 (PowerDynSimEnvDef_v5.py)
  The server will be started automatically when the grid environment
  ``PowerDynSimEnv`` is created.
 - To launch the training, you need first activate the virtual 
-environment. Then run the following scripts. 
-```trainKundur2areaGenBrakingAgent.py``` is used for training 
-the generator braking agent for the Kundur 2-area system and ```trainIEEE39LoadSheddingAgent_*.py``` is used for training an agent for regional load shedding in IEEE 39-bus system
-
+environment. Then run the training scripts under the <examples> folder
 
 ```
 source activate <your-env-name> 
-cd RLGC/src/py  
+cd RLGC/examples/IEEE39_load_shedding/  
 python trainIEEE39LoadSheddingAgent_discrete_action.py 
 ```
 

diff --git a/docs/Dev_notes b/docs/Dev_notes
diff --git a/docs/Makefile b/docs/Makefile
diff --git a/docs/make.bat b/docs/make.bat
diff --git a/docs/source/conf.py b/docs/source/conf.py
diff --git a/docs/source/index.rst b/docs/source/index.rst
diff --git a/examples/IEEE300_load_shedding/test_IEEE300_Zone1_loadshedding.py b/examples/IEEE300_load_shedding/test_IEEE300_Zone1_loadshedding.py
@@ -6,7 +6,7 @@
 
 java_port = 25338
 
-jar_file = '/lib/RLGCJavaServer1.0.0_alpha.jar'
+jar_file = '/lib/RLGCJavaServer1.0.0_rc.jar'