Exemple #1
0
        private void UpdatePool(GAAgent agent, int i)
        {
            if (!agent.isAddedToPool)
            {
                if (pool.name.Equals("Prey Pool"))
                {
                    Debug.Log("Removing " + i);
                }

                agent.Disable();
                agent.isAddedToPool = true;

                m_AgentPool.Add(agent);
                m_AgentPool.Sort();

                agent.owner.transform.SetParent(pool.transform);

                if (m_AgentPool.Count > m_MaxPoolSize)
                {
                    MonoBehaviour.Destroy(m_AgentPool [m_AgentPool.Count - 1].owner);
                    m_AgentPool.RemoveAt(m_AgentPool.Count - 1);
                }

                if (pool.name.Equals("Prey Pool"))
                {
                    Debug.Log("Pool size " + m_AgentPool.Count);
                }
            }
        }
Exemple #2
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        private GAAgent FitnessProportionateSelection()
        {
            float randomSlice = Utilities.instance.RandomMinMax(0, m_TotalFitnessScore);

            GAAgent choosenAgent = null;

            float fitnessTotal = 0;

            for (int i = 0; i < m_AgentPool.Count; i++)
            {
                fitnessTotal += m_AgentPool[i].timeAlive;

                if (fitnessTotal > randomSlice)
                {
                    choosenAgent = m_AgentPool[i];
                    break;
                }

                if (pool.name.Equals("Prey Pool"))
                {
                    Debug.Log("Chose " + i + " of " + m_AgentPool.Count);
                }
            }

            return(choosenAgent);
        }
Exemple #3
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        public NeuralNet CrossOver(GAAgent parentOne, GAAgent parentTwo)
        {
            NeuralNet    neuralNet        = new NeuralNet(m_AnnInput, m_AnnOutput, m_AnnHiddenLayers, m_AnnNeuronsPerHiddenLayer);
            List <float> newWeights       = new List <float> ();
            List <float> parentOneWeights = parentOne.GetNeuralNetwork().GetWeights();
            List <float> parentTwoWeights = parentTwo.GetNeuralNetwork().GetWeights();

            int crossOverPoint;

            if (m_RandomCrossoverPoint)
            {
                crossOverPoint = (int)Utilities.instance.RandomMinMax(0, parentOneWeights.Count);
            }
            else
            {
                crossOverPoint = (int)(parentOneWeights.Count * 0.5f);
            }

            for (int i = 0; i < crossOverPoint; i++)
            {
                newWeights.Add(parentOneWeights [i]);
            }

            for (int i = crossOverPoint; i < parentOneWeights.Count; i++)
            {
                newWeights.Add(parentTwoWeights [i]);
            }

            neuralNet.SetWeights(newWeights);

            return(neuralNet);
        }
Exemple #4
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        private void CreateAgentFromCrossover(GAAgent parentOne, GAAgent parentTwo, int index)
        {
            NeuralNet neuralNetwork = CrossOver(parentOne, parentTwo);

            var pos   = new Vector2(Random.Range(-10f, 10f), Random.Range(-10f, 10f));
            var agent = (GameObject)MonoBehaviour.Instantiate(m_AgentPrefab, pos, Quaternion.identity);

            m_Agents [index] = agent.GetComponent <GAAgent> ();

            m_Agents [index].SetNeuralNetwork(neuralNetwork);

            if (Utilities.instance.RandomMinMax(0f, 1f) < m_MutationChance)
            {
                m_Agents [index].Mutate();
            }

            UpdateGenerationNumber();
        }
Exemple #5
0
 void Awake()
 {
     m_Agent = GetComponent <GAAgent>();
 }