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/*********************************************************************
MLDemos: A User-Friendly visualization toolkit for machine learning
Copyright (C) 2010 Basilio Noris
Contact: mldemos@b4silio.com
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Library General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free
Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*********************************************************************/
#include "public.h"
#include "basicMath.h"
#include <drawUtils.h>
#include <algorithm>
#include "reinforcementDP.h"
#include <QDebug>
using namespace std;
ReinforcementDP::ReinforcementDP()
{
dim = 2;
variance = 0;
bBatchUpdate = false;
maximum = directions = fvec();
}
ReinforcementDP::~ReinforcementDP()
{
}
void ReinforcementDP::SetParams(float variance, bool bBatchUpdate)
{
this->variance = variance;
this->bBatchUpdate = bBatchUpdate;
}
void ReinforcementDP::Initialize(ReinforcementProblem *problem)
{
this->problem = problem;
bConverged = false;
dim = problem->gridSize*problem->gridSize;
directions = problem->directions;
maximum = directions;
float value = problem->GetReward(maximum);
maximumValue = 0;
history.push_back(maximum);
historyValue.push_back(value);
evaluations = 0;
}
fvec ReinforcementDP::Update()
{
if(bConverged) return maximum;
int quantizeType = problem->quantizeType;
int gridSize = problem->gridSize;
fvec newSample = maximum;
fvec &stateValues = problem->stateValues;
// for each tested state, we go through the whole thing and update the closest basis function
FOR(x, gridSize)
{
FOR(y, gridSize)
{
int index = y*gridSize + x;
// we look for the action values for each action
float Q[8];
Q[0] = x < gridSize-1 ? stateValues[(x+1) + (y)*gridSize] : 0.f;
Q[1] = x < gridSize-1 && y < gridSize -1 ? stateValues[(x+1) + (y+1)*gridSize] : 0.f;
Q[2] = y < gridSize-1 ? stateValues[(x) + (y+1)*gridSize] : 0.f;
Q[3] = x>0 && y < gridSize-1 ? stateValues[(x-1) + (y+1)*gridSize] : 0.f;
Q[4] = x>0 ? stateValues[(x-1) + (y)*gridSize] : 0.f;
Q[5] = x>0 && y>0 ? stateValues[(x-1) + (y-1)*gridSize] : 0.f;
Q[6] = y>0 ? stateValues[(x) + (y-1)*gridSize] : 0.f;
Q[7] = x < gridSize-1 && y>0 ? stateValues[(x+1) + (y-1)*gridSize] : 0.f;
int maxIndex=0;
float maxVal = Q[0];
FOR(i, 8)
{
if(maxVal < Q[i])
{
maxVal = Q[i];
maxIndex = i;
}
if(quantizeType==2) i++;
}
switch(quantizeType)
{
case 0:
{
float angle=maxIndex*M_PI*2/8;
newSample[index] = angle;
//newSample[index] = newSample[index]*(1.f-variance) + maxIndex*M_PI*2/8*variance; // we compute the new direction in radians
}
break;
case 1:
newSample[index] = maxIndex;
break;
case 2:
newSample[index] = maxIndex/2;
break;
}
}
}
visited.push_back(newSample);
float value = problem->GetReward(newSample);
evaluations++;
if(value > maximumValue)
{
maximum = newSample;
maximumValue = value;
}
directions = maximum;
history.push_back(maximum);
historyValue.push_back(maximumValue);
newSample = maximum;
return newSample;
}
void ReinforcementDP::Draw(QPainter &painter)
{
int w = painter.viewport().width(), h = painter.viewport().height();
int graphW = 200, graphH = 100, graphPad = 10;
int top = h - 10 - (graphH + 2*graphPad);
int left = 10;
QPainter::RenderHints hints = painter.renderHints();
painter.setRenderHint(QPainter::Antialiasing, false);
QFont font = painter.font();
font.setPointSize(9);
painter.setFont(font);
// we draw the rectangle behind
painter.setOpacity(1);
painter.setBrush(Qt::NoBrush);
painter.setPen(QPen(Qt::black, 2));
painter.drawRect(left, top, graphW + 2*graphPad, graphH + 2*graphPad);
painter.setOpacity(0.6);
painter.setPen(Qt::NoPen);
painter.setBrush(Qt::white);
painter.drawRect(left, top, graphW + 2*graphPad, graphH + 2*graphPad);
painter.setOpacity(1);
painter.setBrush(Qt::black);
painter.setPen(Qt::black);
// we draw the values
double maxValue = -DBL_MAX;
FOR(i, historyValue.size()) maxValue = max(maxValue, historyValue[i]);
int valueLimit = 4;
double upperBound = ((int)ceil(maxValue)/valueLimit + 1)*valueLimit;
painter.setPen(QPen(Qt::black, 2));
QPointF oldPoint;
FOR(i, graphW)
{
int index = i*historyValue.size()/graphW;
QPointF point(i, graphH*(1.f - (historyValue[index]/upperBound)));
point += QPointF(left + graphPad, top + graphPad);
if(i) painter.drawLine(point, oldPoint);
if(i==graphW-1)
{
painter.drawText(point + QPointF(-20,0), QString("%1").arg(historyValue.back(), 0, 'f', 2));
}
oldPoint = point;
}
// we draw the axes
painter.setPen(QPen(Qt::black, 2));
painter.drawLine(left + graphPad, top+graphPad, left + graphPad, top+graphPad + graphH);
painter.drawLine(left + graphPad, top+graphPad+graphH, left + graphPad + graphW, top+graphPad + graphH);
painter.drawText(left + graphPad, top + graphPad, QString("%1").arg(upperBound, 0, 'f', 1));
painter.drawText(left + graphPad, top + graphPad*2 + graphH, QString("0"));
font.setPointSize(9);
painter.setFont(font);
painter.drawText(left, top, graphPad*2 + graphW, graphPad, Qt::AlignCenter, "Maximum Reward");
painter.setRenderHints(hints);
}
const char *ReinforcementDP::GetInfoString()
{
char *text = new char[1024];
if(variance == 0) sprintf(text, "DP Search");
else sprintf(text, "DP Walk\n");
return text;
}
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