protected virtual void Reset()
    {
        pause = true;
        Debug.Log("Entered resetting");
        //Posoruj agentów pod względem funkcji przystosowania
        List <GameObject> SortedList = agents.OrderByDescending(c => c.GetComponent <Fitness>().GetFitness()).ToList();

        agents = SortedList;
        Debug.Log("Sorted cars");

        int chromosomeSize = agents[0].GetComponent <Core>().GetWeights().Length;

        //Preserve car with best fitness
        if (agents[0].GetComponent <Fitness>().GetFitness() > bestFitness)
        {
            bestFitness = agents[0].GetComponent <Fitness>().GetFitness();
            bestCar     = new double[chromosomeSize];
            agents[0].GetComponent <Core>().GetWeights().CopyTo(bestCar, 0);
            Debug.Log("New best fitness: " + bestFitness);
        }

        if (agents[0].GetComponent <Fitness>().GetFitness() >= 306)
        {
            //SavePopulation();
        }

        //save log
        Debug.Log("Calculating avg fitness");
        float avgFitness = 0;

        for (int i = 0; i < agents.Count; i++)
        {
            avgFitness += agents[i].GetComponent <Fitness>().GetFitness();
        }
        avgFitness /= agents.Count;
        string line = generation + " " + agents[0].GetComponent <Fitness>().GetFitness() + " " +
                      bestFitness + " " + avgFitness;

        log.Add(line);
        Debug.Log("Saving log");
        SaveLog();

        //Pobierz wartosci funkcji przystosowania
        //od agentow
        double[] fitness = new double[agents.Count];
        for (int i = 0; i < agents.Count; i++)
        {
            fitness[i] = agents[i].GetComponent <Fitness>().GetFitness();
        }

        Debug.Log("Creating new children");
        double[][] childrenWeights = new double[amount][];
        for (int i = 0; i < amount; i++)
        {
            childrenWeights[i] = new double[chromosomeSize];
        }

        //Assign best car and best car from current generation
        //childrenWeights[0] = cars[0].GetComponent<NeuralNetwork2>().GetWeights();
        agents[0].GetComponent <Core>().GetWeights().CopyTo(childrenWeights[0], 0);
        if (bestCar != null)
        {
            bestCar.CopyTo(childrenWeights[1], 0);
        }
        else
        {
            agents[1].GetComponent <Core>().GetWeights().CopyTo(childrenWeights[1], 0);
        }

        Debug.Log("Creating new generation");
        int index = 2;
        int limit = amount;

        if (addNewEachGen)
        {
            limit = amount - 2;
        }
        while (index < limit)
        {
            //Wybierz indeksy rodzicow
            int parent1index = genetics.rouletteSelect(fitness);
            int parent2index = genetics.rouletteSelect(fitness);

            //Upewnij sie ze rodzice nie sa tacy sami
            while (parent1index == parent2index)
            {
                parent2index = genetics.rouletteSelect(fitness);
            }

            //Pobierz wagi rodzicow
            double[] parent1 = agents[parent1index].GetComponent <Core>().GetWeights();
            double[] parent2 = agents[parent2index].GetComponent <Core>().GetWeights();

            //Dokonaj krzyzowania
            double[] child1;
            double[] child2;
            genetics.Crossover(parent1, parent2, out child1, out child2, crossType);

            child1.CopyTo(childrenWeights[index], 0);
            index++;
            child2.CopyTo(childrenWeights[index], 0);
            index++;
            //Debug.Log("Parent1: " + parent1index + ", parent2: " + parent2index);
        }

        while (index < amount)
        {
            Debug.Log("nowe typy");
            double[] randWeights = agents[0].GetComponent <Core>().GetRandomWeights();
            randWeights.CopyTo(childrenWeights[index], 0);
            index++;
        }

        Debug.Log("Starting mutation");
        //Mutate
        for (int i = 0; i < childrenWeights.Length; i++)
        {
            System.Random rnd = new System.Random((int)DateTime.Now.Ticks);
            if (rnd.NextDouble() < mutationProbability)
            {
                genetics.Mutation(childrenWeights[i], mutationProbability);
            }
        }
        if (mutationType == Genetics.MutationType.IRREGULAR)
        {
            mutationProbability -= 0.0033f;
            Debug.Log("Mutation probability: " + mutationProbability);
            if (mutationProbability < 0.01)
            {
                mutationProbability = 0.01f;
            }
        }


        for (int i = 0; i < agents.Count; i++)
        {
            agents[i].GetComponent <Fitness>().Reset();
            agents[i].GetComponent <Core>().SetWeights(childrenWeights[i]);
            agents[i].transform.position = spawnPoint.position + GetRandVec();
            agents[i].transform.rotation = spawnPoint.rotation;
            //cars[i].GetComponent<FitnessScript>().StartCar();
        }

        //agents[0].GetComponent<Renderer>().material.color = new Color(0, 1, 0);

        generation++;
        Debug.Log("Current generation: " + generation + ". Best fitness so far: " + bestFitness);
    }