From 18059ff25ce02b563a72190c518e2ac9a53aef48 Mon Sep 17 00:00:00 2001
From: "Anmulwar, Sweta V (PG/R - Elec Electronic Eng)" <sa02405@surrey.ac.uk>
Date: Sun, 9 Apr 2023 16:20:10 +0100
Subject: [PATCH] Changed rho alpha beta values
---
.vs/QLearningApp/v16/.suo | Bin 98304 -> 100864 bytes
.../MachineLearning/Models/QLearning.cs | 15 ++--
QLearningApp/Program.cs | 66 +++++++++++++++++-
QLearningApp/RLsync.cs | 12 ++--
QLearningApp/bin/Debug/QLearningApp.exe | Bin 13312 -> 14336 bytes
QLearningApp/bin/Debug/QLearningApp.pdb | Bin 46592 -> 48640 bytes
...QLearningApp.csprojAssemblyReference.cache | Bin 424 -> 424 bytes
QLearningApp/obj/Debug/QLearningApp.exe | Bin 13312 -> 14336 bytes
QLearningApp/obj/Debug/QLearningApp.pdb | Bin 46592 -> 48640 bytes
9 files changed, 77 insertions(+), 16 deletions(-)
diff --git a/.vs/QLearningApp/v16/.suo b/.vs/QLearningApp/v16/.suo
index 4440aaa92ef0803fdda288649133ffcf000c393b..103c2119432c3a8456e8ba30ad974c5e03404fac 100644
GIT binary patch
delta 4269
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diff --git a/QLearningApp/MachineLearning/Models/QLearning.cs b/QLearningApp/MachineLearning/Models/QLearning.cs
index 4734e96..acd1ef5 100644
--- a/QLearningApp/MachineLearning/Models/QLearning.cs
+++ b/QLearningApp/MachineLearning/Models/QLearning.cs
@@ -115,15 +115,15 @@ namespace QLearningApp.MachineLearning
//Console.WriteLine("Enter targetted FPS value ={0}",targetFPS);
double currentState = initialState;//FPS value
- //int counter = 0;
+ int counter = 0;
while (true)
{
currentState = TakeAction(currentState);//currentState= FPS, return should be FPS value
- //counter++;
- /*if (counter == 500)
+ counter++;
+ if (counter == 2000)
break;//will run 500 times*/
- if (_qLearningProblem.GoalStateIsReached(currentState))
- break;
+ /*if (_qLearningProblem.GoalStateIsReached(currentState))
+ break;*/
}
}
@@ -131,7 +131,6 @@ namespace QLearningApp.MachineLearning
{
//var validActions = _qLearningProblem.GetValidActions(currentState);
int randomIndexActionGamma = _random.Next(0, _qLearningProblem.gamma_length);
-
int randomIndexActionDelta = _random.Next(0, _qLearningProblem.delta_length);
//Console.WriteLine("randomIndexActionGamma ={0},randomIndexActionDelta ={1}", randomIndexActionGamma, randomIndexActionDelta);
//int randomIndexActionDelta = _random.Next(0, _qLearningProblem.NumberOfActions);//choosing a random action from number of actions defined
@@ -139,7 +138,7 @@ namespace QLearningApp.MachineLearning
// double delta_action = _qLearningProblem.delta_space[randomIndexActionGamma];
double delta_action = _qLearningProblem.gamma_space[randomIndexActionDelta]; //fixed for now, can be changed in the future
//int delta_actionIndex = 0;
- double rho = 0.4;
+ double rho = 0.1;//0.4;
double saReward = _qLearningProblem.GetReward(currentState, gamma_action, delta_action);
// Use rho to have a slowly & smoothly changing FPS value after an action is taken
@@ -162,7 +161,7 @@ namespace QLearningApp.MachineLearning
double nsReward = Max;
//Console.WriteLine("value of nsReward=" + nsReward);
- double qCurrentState = saReward + (_gamma * nsReward);
+ double qCurrentState = saReward + (_gamma * nsReward);//discount factor=0.8
int currentStateIndex = _qLearningProblem.fps_spaceIndex(currentState);
_qTable[currentStateIndex, randomIndexActionGamma, randomIndexActionDelta] = qCurrentState;
return nextState;
diff --git a/QLearningApp/Program.cs b/QLearningApp/Program.cs
index 776be1c..c9d9244 100644
--- a/QLearningApp/Program.cs
+++ b/QLearningApp/Program.cs
@@ -18,7 +18,7 @@ namespace QLearningApp
var qLearning = new QLearning(0.8, new RLSync(),targetFPS);
Console.WriteLine("Training Agent...");
- qLearning.TrainAgent(2000);
+ qLearning.TrainAgent(3000);
Console.WriteLine("step 2");
Console.WriteLine("Training is Done!");
@@ -27,6 +27,8 @@ namespace QLearningApp
Console.ReadLine();
do
{
+ double[,] Qdisplay = new double[6, 6];
+
Console.WriteLine($"Enter initial state Index. Number between 0 and the number of states (5). Press 'q' to exit...");
int initialStateIndex = 0;
if (!int.TryParse(Console.ReadLine(), out initialStateIndex)) break;
@@ -35,15 +37,75 @@ namespace QLearningApp
{
var qLearningStats = qLearning.Run(initialStateIndex);
Console.WriteLine(qLearningStats.ToString());
+ /* Display 3 dimesional Q matrix */
+
+ for (int k = 0; k < qLearning.QTable.GetLength(0); k++)
+ {
+ for (int i = 0; i < qLearning.QTable.GetLength(1); i++)
+ {
+ for (int j = 0; j < qLearning.QTable.GetLength(2); j++)
+ {
+ Qdisplay[i, j] = qLearning.QTable[k, i, j];
+ //Console.Write(Math.Round(Qdisplay[i, j] * 10) / 10 + "\t");
+ }
+ }
+ /* To print normalized matrix */
+ var normalizedMatrix = NormalizeMatrix(Qdisplay);
+ for (int i = 0; i < Qdisplay.GetLength(0); i++)
+ {
+ for (int j = 0; j < Qdisplay.GetLength(1); j++)
+ {
+
+ Console.Write(Math.Round(normalizedMatrix[i, j] * 10) / 10 + "\t");
+ }
+ Console.WriteLine();
+ }
+ Console.WriteLine();
+ }
+
+ //Console.Write(Qdisplay.ToString());
+ //var normalizedMatrix = NormalizeMatrix(Qdisplay);
+ //Console.Write(qLearning.QTable.ToString());
//var normalizedMatrix = qLearning.QTable.NormalizeMatrix();
//Console.Write(qLearning.QTable.ToString());
}
- catch(Exception ex)
+ catch (Exception ex)
{
Console.WriteLine($"Error: {ex.Message}");
}
}
while (true);
}
+ public static double[,] NormalizeMatrix(double[,] matrix)
+ {
+ /* To normalize matrix*/
+ var maxElement = GetMaxElement(matrix);
+
+ var normalizedMatrix = new double[6,6];
+ for (int i = 0; i < matrix.GetLength(0); i++)//0-rows, 1-col,2-z values
+ {
+ //normalizedMatrix[i] = new double[matrix.Length(0)];
+ for (int j = 0; j < matrix.GetLength(1); j++)
+ {
+ if (matrix[i,j] != 0)
+ normalizedMatrix[i,j] = Math.Round((matrix[i, j] * 100) / maxElement, 0);
+ }
+ }
+ return normalizedMatrix;
+ }
+
+ public static double GetMaxElement(double[,] matrix)
+ {
+ double maxElement = 0;
+ for (int i = 0; i < matrix.GetLength(0); i++)
+ {
+ for (int j = 0; j < matrix.GetLength(1); j++)
+ {
+ if (matrix[i,j] > maxElement)
+ maxElement = matrix[i,j];
+ }
+ }
+ return maxElement;
+ }
}
}
diff --git a/QLearningApp/RLsync.cs b/QLearningApp/RLsync.cs
index 2d50828..344e77c 100644
--- a/QLearningApp/RLsync.cs
+++ b/QLearningApp/RLsync.cs
@@ -12,8 +12,8 @@ namespace QLearningApp
public double[] delta_space = { 10, 20, 30, 40, 50, 100 }; // possible values of delta- 6 values
//public double[] delta_space = { 20 };
- public double alpha = 0.2; //original value
- public double beta = 0.2;//original value
+ public double alpha = 0.2; //original value 0.4
+ public double beta = 0.2;//original value 0.4
// State space
public double[] fps_space = { 10, 12, 15, 17, 20, 22, 25, 27, 30 };//PE, APL 0-30 read it form file-9 states
@@ -21,10 +21,10 @@ namespace QLearningApp
private double[][] rewards = new double[5][];
public double Target_fps { get; set;}//made it a proerty
/*
- target for syn window if it is 100ms, target should be given by user not hard-coded
+ target for syn window if it is 100ms, target should be given by user not hard-coded - done
ini satse: 20 FPS, 100ms latency
initial values 30FPS, APL 100ms--> Target APL 20ms, FPS >=15
- FPS, PE: 30FPS, PE: 25ms --> target
+ FPS, PE: 30FPS, PE: 25ms --> target
trade -off
*/
@@ -38,7 +38,7 @@ namespace QLearningApp
{
//double reward = 1 / (System.Math.Abs(currentState - target_fps)) - alpha * action_gamma - beta * action_delta;
//double reward = 1 / (1+System.Math.Abs(currentState - target_fps)) - alpha * action_gamma/100 - beta * action_delta/100; // gamma nand delta are normalized to [0, 1]; does not perform well when fps is very low
- double reward = 1 / (1 + System.Math.Abs(currentState - Target_fps)) * (1 - alpha * System.Math.Abs(action_gamma / 100) - beta * System.Math.Abs(action_delta / 100)); // gamma nand delta are normalized to [0, 1]
+ double reward = 1 / (1 + System.Math.Abs(currentState - Target_fps)) * (1 - alpha * action_gamma / 100 - beta * action_delta / 100); // gamma nand delta are normalized to [0, 1], so that there is always a positive reward
return reward;
}
@@ -53,7 +53,7 @@ namespace QLearningApp
{ 8, 10, 13, 15, 18, 20 },
{ 10, 12, 15, 20, 22, 25 },
{ 10, 15, 18, 20, 25, 28 },
- { 30, 30, 30, 30, 30, 30 },
+ { 10, 15, 20, 22, 30, 30 },
{ 15, 20, 25, 27, 30, 30 } }; //{ 10, 15, 20, 22, 30, 30 }-second last row
// 6*6 matrix Further improvements in the simulation: depending on changing network delays, the FPS matrix can change, allowing higher FPS at smaller values of gamma and delta
return System.Math.Abs(FPS[gammaIndex,deltaIndex] + random_fps_noise);
diff --git a/QLearningApp/bin/Debug/QLearningApp.exe b/QLearningApp/bin/Debug/QLearningApp.exe
index ea93ca9266419cad2c275deb93337a461f88c6f1..69860436b49a7b74247d4dd3d9d92820600b4015 100644
GIT binary patch
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diff --git a/QLearningApp/bin/Debug/QLearningApp.pdb b/QLearningApp/bin/Debug/QLearningApp.pdb
index 212d01b5aaf1be5750912b2a43e1e1ccbc55e85b..64633aa518b875e67a87556f189d8bc4ade484fb 100644
GIT binary patch
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diff --git a/QLearningApp/obj/Debug/QLearningApp.csprojAssemblyReference.cache b/QLearningApp/obj/Debug/QLearningApp.csprojAssemblyReference.cache
index c240c2684ac24abfea7de1369b85f99398d906b7..81441d12ea59e70c6bb50a099f24c3d712ed2b57 100644
GIT binary patch
delta 13
UcmZ3%yn=ayKO^I>$pMTJ03kdCjsO4v
delta 13
UcmZ3%yn=ayKO^IU$pMTJ03Uh;Gynhq
diff --git a/QLearningApp/obj/Debug/QLearningApp.exe b/QLearningApp/obj/Debug/QLearningApp.exe
index ea93ca9266419cad2c275deb93337a461f88c6f1..69860436b49a7b74247d4dd3d9d92820600b4015 100644
GIT binary patch
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delta 4100
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diff --git a/QLearningApp/obj/Debug/QLearningApp.pdb b/QLearningApp/obj/Debug/QLearningApp.pdb
index 212d01b5aaf1be5750912b2a43e1e1ccbc55e85b..64633aa518b875e67a87556f189d8bc4ade484fb 100644
GIT binary patch
delta 6884
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AyZ`_I
delta 5128
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