From a8964e1c8a54e74c439a8907757ec8792aca14d2 Mon Sep 17 00:00:00 2001
From: "Anmulwar, Sweta V (PG/R - Elec Electronic Eng)" <sa02405@surrey.ac.uk>
Date: Sat, 18 Mar 2023 18:34:58 +0000
Subject: [PATCH] change reward function
---
.vs/QLearningApp/v16/.suo | Bin 85504 -> 94208 bytes
.../MachineLearning/Models/QLearning.cs | 17 +++++++----
.../MachineLearning/Models/QLearningStats.cs | 8 ++++--
QLearningApp/RLsync.cs | 27 +++++++++++++-----
QLearningApp/bin/Debug/QLearningApp.exe | Bin 11776 -> 11776 bytes
QLearningApp/bin/Debug/QLearningApp.pdb | Bin 42496 -> 44544 bytes
...QLearningApp.csprojAssemblyReference.cache | Bin 424 -> 1887 bytes
QLearningApp/obj/Debug/QLearningApp.exe | Bin 11776 -> 11776 bytes
QLearningApp/obj/Debug/QLearningApp.pdb | Bin 42496 -> 44544 bytes
9 files changed, 37 insertions(+), 15 deletions(-)
diff --git a/.vs/QLearningApp/v16/.suo b/.vs/QLearningApp/v16/.suo
index bc59d5026d25c5db7010cc3e41ab3f114900bd4d..c11f9588c5155375b9b6326e09a8499ec865b4e8 100644
GIT binary patch
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diff --git a/QLearningApp/MachineLearning/Models/QLearning.cs b/QLearningApp/MachineLearning/Models/QLearning.cs
index cf3e1d5..2939919 100644
--- a/QLearningApp/MachineLearning/Models/QLearning.cs
+++ b/QLearningApp/MachineLearning/Models/QLearning.cs
@@ -49,24 +49,29 @@ namespace QLearningApp.MachineLearning
int stateIndex = initialStateIndex;
int counter = 0;
List<double> actions = new List<double>();
+ List<double> stateFPS = new List<double>();
+ double prev_state = result.InitialState;
while (true)
{
result.Steps += 1;
counter++;
int actionIndex = _qTable[stateIndex].ToList().IndexOf(_qTable[stateIndex].Max());
-
//state = action;
double state = _qLearningProblem.SimulatedFPSValues(actionIndex, 4);
+
double statefps_space = _qLearningProblem.fps_space.OrderBy(x => Math.Abs((long)x - state)).First();
stateIndex = _qLearningProblem.fps_spaceIndex(statefps_space);
double gamma_action = _qLearningProblem.gamma_space[actionIndex];
+ Console.WriteLine("Current state: " + prev_state + ", Action: " + gamma_action + ", Next state: " + state);
+ prev_state = state;
actions.Add(gamma_action);
+ stateFPS.Add(state);
/*if (_qLearningProblem.GoalStateIsReached(action))
{
result.EndState = action;
break;
}*/
- if (counter == 100)
+ if (counter == 50)
{ break; }
}
result.Actions = actions.ToArray();
@@ -82,8 +87,8 @@ namespace QLearningApp.MachineLearning
{
currentState = TakeAction(currentState);//currentState= FPS, return should be FPS value
counter++;
- if (counter == 100)
- break;//will run 100 times
+ if (counter == 500)
+ break;//will run 500 times
}
}
@@ -96,9 +101,11 @@ namespace QLearningApp.MachineLearning
// double delta_action = _qLearningProblem.delta_space[randomIndexActionGamma];
double delta_action = 20; //fixed for now, can be changed in the future
int delta_actionIndex = 0;
+ double rho = 0.4;
double saReward = _qLearningProblem.GetReward(currentState, gamma_action, delta_action);
- double nextState = _qLearningProblem.SimulatedFPSValues(randomIndexActionGamma, delta_actionIndex); // FPS for time t+1, delta_actionIndex changes to 4 in the RLSync as it is hardcoded
+ // Use rho to have a slowly & smoothly changing FPS value after an action is taken
+ double nextState = rho * currentState + (1-rho) * _qLearningProblem.SimulatedFPSValues(randomIndexActionGamma, delta_actionIndex); // FPS for time t+1, delta_actionIndex changes to 4 in the RLSync as it is hardcoded
double nextStatefps_space = _qLearningProblem.fps_space.OrderBy(x => Math.Abs((long)x - nextState)).First();//need to test first, looking for the closet value of the FPS
int nextStateIndex = _qLearningProblem.fps_spaceIndex(nextStatefps_space);
double nsReward = _qTable[nextStateIndex].Max();
diff --git a/QLearningApp/MachineLearning/Models/QLearningStats.cs b/QLearningApp/MachineLearning/Models/QLearningStats.cs
index 52197bf..3cbb266 100644
--- a/QLearningApp/MachineLearning/Models/QLearningStats.cs
+++ b/QLearningApp/MachineLearning/Models/QLearningStats.cs
@@ -9,14 +9,16 @@ namespace QLearningApp.MachineLearning
public int Steps { get; set; }
public double[] Actions { get; set; }
+ public double[] State { get; set; }
+
public override string ToString()
{
StringBuilder sb = new StringBuilder();
sb.AppendLine($"Agent needed {Steps} steps to find the solution");
sb.AppendLine($"Agent Initial State: {InitialState}");
- foreach (var action in Actions)
- sb.AppendLine($"Action: {action}");
- sb.AppendLine($"Agent arrived at the goal state: {EndState}");
+ /*foreach (var action in Actions)
+ sb.AppendLine($"Action: {action}");*/
+ //sb.AppendLine($"Agent arrived at the goal state: {EndState}");
return sb.ToString();
}
}
diff --git a/QLearningApp/RLsync.cs b/QLearningApp/RLsync.cs
index ba89edc..53bd0f8 100644
--- a/QLearningApp/RLsync.cs
+++ b/QLearningApp/RLsync.cs
@@ -1,23 +1,32 @@
using QLearningApp.MachineLearning;
+using System;
using System.Collections.Generic;
namespace QLearningApp
{
public class RLSync
{
+ private Random _random = new Random();
// Actions
public double[] gamma_space = { 10, 20, 30, 40, 50, 100 }; // the possible values of gamma
// private double[] delta_space = { 10, 20, 30, 40, 50, 100 }; // possible values of delta
public double[] delta_space = { 20 };
- public double alpha = 0.5; //original value
- public double beta = 0.5;//original value
+ public double alpha = 0.2; //original value
+ public double beta = 0.2;//original value
// State space
- public double[] fps_space = { 10, 15, 20, 25, 30 };
+ public double[] fps_space = { 10, 12, 15, 17, 20, 22, 25, 27, 30 };//PE, APL 0-30 read it form file
private double[][] rewards = new double[5][];
public double target_fps = 30;
+ /*
+ target for syn window if it is 100ms, target should be given by user not hard-coded
+ ini satse: 20 FPS, 100ms latency
+ initial values 30FPS, APL 100ms--> Target APL 20ms, FPS >=15
+ FPS, PE: 30FPS, PE: 25ms --> target
+ trade -off
+ */
public int NumberOfStates => fps_space.Length;
@@ -26,17 +35,21 @@ namespace QLearningApp
public double GetReward(double currentState, double action_gamma, double action_delta)
{
//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]
+ //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 * action_gamma / 100 - beta * action_delta / 100); // gamma nand delta are normalized to [0, 1]
return reward;
}
public double SimulatedFPSValues(int gammaIndex, int deltaIndex) //these values are for trial and this is the simulated environment
{
-
+
//keeping deltaIndex as 4 so that RL algo could get the FPS 30 value, changed it to 2 to get the better Q values
- deltaIndex = 4;
+ deltaIndex = _random.Next(3,5);//trying randomness
+ double random_fps_noise = 3*(_random.NextDouble()-0.5); // adding uniform random noise to observed fps value
+ // deltaIndex = 3;
double[,] FPS = new double[,] { { 0, 10, 10, 15, 15, 15 }, { 10, 12, 13, 15, 20, 22 }, { 10, 15, 15, 20, 25, 25 }, { 10, 20, 20, 25, 30, 30 }, { 10, 20, 25, 30, 30, 30 }, { 25, 25, 27, 30, 30, 30 } };
- return FPS[gammaIndex,deltaIndex];
+ // 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 FPS[gammaIndex,deltaIndex] + random_fps_noise;
}
public int fps_spaceIndex(double FPS)
diff --git a/QLearningApp/bin/Debug/QLearningApp.exe b/QLearningApp/bin/Debug/QLearningApp.exe
index dd85ef5d8b654d2ec43c4a78f954a79055056d3f..e4f4aa93e341dc975f26aaa27d9d8eea57239c6a 100644
GIT binary patch
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diff --git a/QLearningApp/bin/Debug/QLearningApp.pdb b/QLearningApp/bin/Debug/QLearningApp.pdb
index 59f8455f6d0cc789d542866017dc0fe548410b34..9d44aaa90b958891a04c1091fe1a6b715ea4889a 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 5485901991f75d811c511f8b31437067d0d8912b..61c22a8f31600f3266c789c4539e43982ba04598 100644
GIT binary patch
literal 1887
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delta 28
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diff --git a/QLearningApp/obj/Debug/QLearningApp.exe b/QLearningApp/obj/Debug/QLearningApp.exe
index dd85ef5d8b654d2ec43c4a78f954a79055056d3f..e4f4aa93e341dc975f26aaa27d9d8eea57239c6a 100644
GIT binary patch
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diff --git a/QLearningApp/obj/Debug/QLearningApp.pdb b/QLearningApp/obj/Debug/QLearningApp.pdb
index 59f8455f6d0cc789d542866017dc0fe548410b34..9d44aaa90b958891a04c1091fe1a6b715ea4889a 100644
GIT binary patch
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