Exemple #1
0
        private Task StartGame(EncogMaze encogMaze)
        {
            Task task = Task.Run(() =>
            {
                encogMaze.Train(TotalIterations, maxTemp, minTemp, encogCycles);
            });

            return(task);
        }
Exemple #2
0
 private async void windowLessTraining_Loaded_1(object sender, RoutedEventArgs e)
 {
     if (isEncog)
     {
         EncogMaze encogMaze = new EncogMaze(maze);
         encogMaze.MazeCycleComplete += Traveler_MazeCycleComplete;
         //encogMaze.EncogCycleComplete += EncogMaze_EncogCycleComplete;
         encogMaze.TrainingIterationComplete += EncogMaze_TrainingIterationComplete;
         await StartGame(encogMaze);
     }
     else
     {
         // reset session count
         //RNNMazeTraveler.ResestStaticSessionData();
         traveler = new RLMMazeTraveler(maze, Learn, Temp_num_sessions, StartRandomness, EndRandomness);
         traveler.MazeCycleComplete += Traveler_MazeCycleComplete;
         traveler.SessionStarted    += Traveler_SessionStarted;
         await StartGame(traveler);
     }
 }
        /// <summary>
        /// This is where encog is being configured and trained. Call this method and pass the needed parameters to start the training.
        /// </summary>
        /// <param name="maze">Contains all the details of the maze that encog will be going to solve.</param>
        /// <param name="gameTimeout">The given time for solving the maze per session.</param>
        public static void MazeTrain(MazeInfo maze, int gameTimeout)
        {
            Console.WriteLine("Encog network structure:");

            int defHiddenLayers     = maze.Width * maze.Height;                                                               //This is the default number of hidden layer neurons if not specified.
            int hiddenLayers        = Util.GetInput("Number of hidden layers [default 1]: ", 1);                              //Getting user input for the number of hidden layers, will be set to 1(default) if not specified.
            int hiddenLayersNeurons = Util.GetInput($"Hidden layers rneurons [default {defHiddenLayers}]:", defHiddenLayers); //Getting user input for the number of hidden layer neurons, will be set to "defHiddenLayers"(default) value if not specified.

            var em = new EncogMaze(maze, hiddenLayers, hiddenLayersNeurons);                                                  //Create an instance of encog maze game to configure the network.

            em.EncogCycleComplete        += SesionComplete;
            em.TrainingIterationComplete += Em_DoneTrainingIteration;

            int mode    = Util.GetInput("Select Encog learning method [Annealing - 0, Genetic - 1 default]: ", 1);               //Gets user input for the type of encog training method, e.g. (Simulated Annealing, MLGeneticAlgorithm)
            int epochs  = Util.GetInput("EPOCHS to execute [default 10]: ", 10);                                                 //Gets user input for the number of epochs
            int cycles  = Util.GetInput((mode == 0) ? "Cycles per epoch [defualt 10]: " : "Population size [default 10]: ", 10); //Gets user input for the number of cycles
            int maxTemp = 0;
            int minTemp = 0;

            if (mode == 0)
            {
                maxTemp = Util.GetInput("Max temperature [default 10]: ", 10); //Gets user input for the starting temperature
                minTemp = Util.GetInput("Min temperature [default 2]: ", 2);   //Gets user input for the ending temperature
            }
            Console.WriteLine();

            System.Diagnostics.Stopwatch watch = new System.Diagnostics.Stopwatch();
            watch.Start();

            em.Train(mode, epochs, maxTemp, minTemp, cycles, gameTimeout); //Call the Train() method from encog game lib to start the training.

            watch.Stop();

            Console.WriteLine($"Elapsed: {watch.Elapsed}");
            Console.ReadLine();
        }