NeuroEvolution.cpp

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/*
 * Copyright © 2012, United States Government, as represented by the
 * Administrator of the National Aeronautics and Space Administration.
 * All rights reserved.
 * 
 * The NASA Tensegrity Robotics Toolkit (NTRT) v1 platform is licensed
 * under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 * http://www.apache.org/licenses/LICENSE-2.0.
 * 
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
 * either express or implied. See the License for the specific language
 * governing permissions and limitations under the License.
*/

#include "NeuroEvolution.h"
#include "learning/Configuration/configuration.h"
#include "core/tgString.h"
#include "helpers/FileHelpers.h"
// The C++ Standard Library
#include <iostream>
#include <numeric>
#include <string>
#include <sstream>
#include <stdexcept>

using namespace std;

#ifdef _WIN32

//  Windows
#define rdtsc  __rdtsc

#else

//  For everything else
unsigned long long rdtsc(){
    unsigned int lo,hi;
    __asm__ __volatile__ ("rdtsc" : "=a" (lo), "=d" (hi));
    return ((unsigned long long)hi << 32) | lo;
}

#endif

NeuroEvolution::NeuroEvolution(std::string suff, std::string config, std::string path) :
suffix(suff)
{
                currentTest=0;
                generationNumber=0;
                if (path != "")
                {
                                resourcePath = FileHelpers::getResourcePath(path);
                }
                else
                {
                                resourcePath = "";
                }

                std::string configPath = resourcePath + config;

    configuration myconfigdataaa;
                myconfigdataaa.readFile(configPath);
                populationSize=myconfigdataaa.getintvalue("populationSize");
    numberOfElementsToMutate=myconfigdataaa.getintvalue("numberOfElementsToMutate");
                numberOfChildren=myconfigdataaa.getintvalue("numberOfChildren");
                numberOfTestsBetweenGenerations=myconfigdataaa.getintvalue("numberOfTestsBetweenGenerations");
                numberOfControllers=myconfigdataaa.getintvalue("numberOfControllers"); //shared with ManhattanToyController
                leniencyCoef=myconfigdataaa.getDoubleValue("leniencyCoef");
                coevolution=myconfigdataaa.getintvalue("coevolution");
    seeded = myconfigdataaa.getintvalue("startSeed");
    
    bool learning = myconfigdataaa.getintvalue("learning");
    
    if (populationSize < numberOfElementsToMutate + numberOfChildren)
    {
        throw std::invalid_argument("Population will grow with given parameters");
    }
    
   srand(rdtsc());
                eng.seed(rdtsc());

                for(int j=0;j<numberOfControllers;j++)
                {
                                cout<<"creating Populations"<<endl;
                                populations.push_back(new NeuroEvoPopulation(populationSize,myconfigdataaa));
                }

    // Overwrite the random parameters based on data
    if(seeded) // Test that the file exists
    {
        for(int i = 0; i < numberOfControllers; i++)
        {
            NeuroEvoMember* seededPop = populations[i]->controllers.back();
            stringstream ss;
            ss<< resourcePath <<"logs/bestParameters-"<<this->suffix<<"-"<<i<<".nnw";
            seededPop->loadFromFile(ss.str().c_str());
        }
    }
    if(learning)
    {
                                evolutionLog.open((resourcePath + "logs/evolution"+suffix+".csv").c_str(),ios::out);
                                if (!evolutionLog.is_open())
                                {
                                                throw std::runtime_error("Logs does not exist. Please create a logs folder in your build directory or update your cmake file");
                                }
    }
}

NeuroEvolution::~NeuroEvolution()
{
                // @todo - solve the invalid pointer that occurs here
                #if (0)
                for(std::size_t i = 0; i < populations.size(); i++)
                {
                                delete populations[i];
                }
                populations.clear();
                #endif
}

void NeuroEvolution::mutateEveryController()
{
                for(std::size_t i=0;i<populations.size();i++)
                {
                                populations.at(i)->mutate(&eng,numberOfElementsToMutate);
                }
}

void NeuroEvolution::combineAndMutate()
{
    for(std::size_t i=0;i<populations.size();i++)
    {
        populations.at(i)->combineAndMutate(&eng, numberOfElementsToMutate, numberOfChildren);
    }    
}

void NeuroEvolution::orderAllPopulations()
{
                generationNumber++;
                double aveScore1 = 0.0;
                double aveScore2 = 0.0;
#if (0)
                // Disable definition of unused variables to suppress compiler warning
                double maxScore1,maxScore2;
#endif
                for(std::size_t i=0;i<scoresOfTheGeneration.size();i++)
                {
                                aveScore1+=scoresOfTheGeneration[i][0];
                                aveScore2+=scoresOfTheGeneration[i][1];
                }
                aveScore1 /= scoresOfTheGeneration.size();
                aveScore2 /= scoresOfTheGeneration.size();


                for(std::size_t i=0;i<populations.size();i++)
                {
                                populations.at(i)->orderPopulation();
                }
                evolutionLog<<generationNumber*numberOfTestsBetweenGenerations<<","<<aveScore1<<","<<aveScore2<<",";
                evolutionLog<<populations.at(0)->getMember(0)->maxScore<<","<<populations.at(0)->getMember(0)->maxScore1<<","<<populations.at(0)->getMember(0)->maxScore2<<endl;
                
                
                // what if member at 0 isn't the best of all time for some reason? 
                // This seems biased towards average scores
                ofstream logfileLeader;
                for(std::size_t i=0;i<populations.size();i++)
                {
                                stringstream ss;
                                ss << resourcePath <<"logs/bestParameters-"<<suffix<<"-"<<i<<".nnw";
                                populations[i]->getMember(0)->saveToFile(ss.str().c_str());
                }
}

double diffclock(clock_t clock1,clock_t clock2)
{
                double diffticks=clock1-clock2;
                double diffms=(diffticks*10)/CLOCKS_PER_SEC;
                return diffms;
}

vector <NeuroEvoMember *> NeuroEvolution::nextSetOfControllers()
{
                int testsToDo=0;
                if(coevolution)
                                testsToDo=numberOfTestsBetweenGenerations; //stop when we reach x amount of random tests
                else
                                testsToDo=populationSize; //stop when we test each element once

                if(currentTest == testsToDo)
                {
                                orderAllPopulations();
        if (numberOfChildren == 0)
        {
            mutateEveryController();
        }
        else
        {
            combineAndMutate();
        }
                                cout<<"mutated the populations"<<endl;
                                this->scoresOfTheGeneration.clear();

                                if(coevolution)
                                                currentTest=0;//Start from 0
                                else
                                                currentTest=populationSize - numberOfElementsToMutate - numberOfChildren; //start from the mutated ones only (last x)
                }

                selectedControllers.clear();
                for(std::size_t i=0;i<populations.size();i++)
                {
                                int selectedOne=0;
                                if(coevolution)
                                                selectedOne=rand()%populationSize; //select random one from each pool
                                else
                                                selectedOne=currentTest; //select the same from each pool

//                              cout<<"selected: "<<selectedOne<<endl;
                                selectedControllers.push_back(populations.at(i)->getMember(selectedOne));
                }
                currentTest++;
//              cout<<"currentTest:"<<currentTest<<endl;

                return selectedControllers;
}

void NeuroEvolution::updateScores(vector <double> multiscore)
{
                if(multiscore.size()==2)
                                this->scoresOfTheGeneration.push_back(multiscore);
                else
                                multiscore.push_back(-1.0);
                double score=1.0* multiscore[0] - 0.0 * multiscore[1];
                for(std::size_t oneElem=0;oneElem<selectedControllers.size();oneElem++)
                {
                                NeuroEvoMember * controllerPointer=selectedControllers.at(oneElem);

                                controllerPointer->pastScores.push_back(score);
                                double prevScore=controllerPointer->maxScore;
                                if(prevScore>score)
                                {
                                                                double newScore= leniencyCoef * prevScore + (1.0 - leniencyCoef) * score;
                                                                controllerPointer->maxScore=newScore;
                                }
                                else
                                {
                                                controllerPointer->maxScore=score;
                                                controllerPointer->maxScore1=multiscore[0];
                                                controllerPointer->maxScore2=multiscore[1];
                                }
                }

                //Record it to the file
                ofstream payloadLog;
                payloadLog.open((resourcePath + "logs/scores.csv").c_str(),ios::app);
                payloadLog<<multiscore[0]<<","<<multiscore[1]<<endl;
                payloadLog.close();
                return;
}