Add genetic algorithm

survival/__init__.py

@@ -1,13 +1,15 @@
 import pygame
 
+from survival.ai.genetic_algorithm import GeneticAlgorithm
 from survival.components.inventory_component import InventoryComponent
 from survival.game.game_map import GameMap
 from survival.generators.building_generator import BuildingGenerator
 from survival.generators.player_generator import PlayerGenerator
 from survival.generators.resource_generator import ResourceGenerator
 from survival.generators.world_generator import WorldGenerator
-from survival.settings import SCREEN_WIDTH, SCREEN_HEIGHT
+from survival.settings import SCREEN_WIDTH, SCREEN_HEIGHT, MUTATE_NETWORKS, LEARN
 from survival.systems.draw_system import DrawSystem
+from survival.systems.neural_system import NeuralSystem
 
 
 class Game:
@@ -15,10 +17,17 @@ class Game:
         self.world_generator = WorldGenerator(win, self.reset)
         self.game_map, self.world, self.camera = self.world_generator.create_world()
         self.run = True
+        if LEARN and MUTATE_NETWORKS:
+            self.genetic_algorithm = GeneticAlgorithm(self.world.get_processor(NeuralSystem), self.finish_training)
 
     def reset(self):
+        if LEARN and MUTATE_NETWORKS:
+            self.genetic_algorithm.train()
         self.world_generator.reset_world()
 
+    def finish_training(self):
+        self.run = False
+
     def update(self, ms):
         events = pygame.event.get()
 
@@ -46,4 +55,4 @@ if __name__ == '__main__':
     game = Game()
 
     while game.run:
-        game.update(clock.tick(60))
+        game.update(clock.tick(500))

survival/ai/genetic_algorithm.py

@@ -0,0 +1,84 @@
+import sys
+
+from survival.ai.model import LinearQNetwork
+from survival.ai.optimizer import Optimizer
+
+
+class GeneticAlgorithm:
+    GAMES_PER_NETWORK = 40
+    PLOTS_COUNTER = 0
+    CURRENT_GENERATION = 1
+
+    def __init__(self, neural_system, callback):
+        self.callback = callback
+        self.logs_file = open('genetic_logs.txt', 'w')
+        self.original_stdout = sys.stdout
+        sys.stdout = self.logs_file
+        self.neural_system = neural_system
+        self.generations = 20
+        self.population = 10  # Minimum 5 needed to allow breeding
+        self.nn_params = {
+            'neurons': [128, 192, 256, 384, 512],
+            'layers': [0, 1, 2, 3],
+            'activation': ['relu', 'elu', 'tanh'],
+            'ratio': [0.0007, 0.0009, 0.0011, 0.0013, 0.0015],
+            'optimizer': ['RMSprop', 'Adam', 'SGD', 'Adagrad', 'Adadelta'],
+        }
+        self.optimizer = Optimizer(self.nn_params)
+        self.networks: list[LinearQNetwork] = self.optimizer.create_population(self.population)
+        self.finished = False
+        self.trained_counter = 0
+        self.iterations = 0
+        self.trained_generations = 0
+        print('Started generation 1...')
+        self.change_network(self.networks[0])
+
+    def train(self):
+        if self.iterations < GeneticAlgorithm.GAMES_PER_NETWORK - 1:
+            self.iterations += 1
+            return
+        self.iterations = 0
+        print(f'Network score: {self.optimizer.fitness(self.networks[self.trained_counter])}')
+        self.trained_counter += 1
+
+        # If all networks in current population were trained
+        if self.trained_counter >= self.population:
+            # Get average score in current population
+            avg_score = self.calculate_average_score(self.networks)
+            print(f'Average population score: {avg_score}.')
+
+            results_file = open('genetic_results.txt', 'w')
+            for network in self.networks:
+                results_file.write(
+                    f'Network {network.id} params {network.network_params}. Avg score = {sum(network.scores) / len(network.scores)}\n')
+            results_file.close()
+            if self.trained_generations >= self.generations - 1:
+                # Sort the final population
+                self.networks = sorted(self.networks, key=lambda x: sum(x.scores) / len(x.scores), reverse=True)
+                self.finished = True
+                self.logs_file.close()
+                sys.stdout = self.original_stdout
+                self.callback()
+                return
+
+            self.trained_generations += 1
+            GeneticAlgorithm.CURRENT_GENERATION = self.trained_generations + 1
+            print(f'Started generation {GeneticAlgorithm.CURRENT_GENERATION}...')
+            self.networks = self.optimizer.evolve(self.networks)
+            self.trained_counter = 0
+        self.change_network(self.networks[self.trained_counter])
+
+    def calculate_average_score(self, networks):
+        sums = 0
+        lengths = 0
+        for network in networks:
+            sums += self.optimizer.fitness(network)
+            lengths += len(network.scores)
+        return sums / lengths
+
+    def change_network(self, net):
+        GeneticAlgorithm.PLOTS_COUNTER += 1
+        print(f"Changed network to {GeneticAlgorithm.PLOTS_COUNTER} {net.network_params}")
+        self.logs_file.flush()
+        net.id = GeneticAlgorithm.PLOTS_COUNTER
+        self.neural_system.load_model(net)

survival/ai/learning_utils.py

@@ -2,6 +2,8 @@ import numpy as np
 from IPython import display
 from matplotlib import pyplot as plt
 
+from survival.settings import MUTATE_NETWORKS
+from survival.ai.genetic_algorithm import GeneticAlgorithm
 from survival.components.learning_component import LearningComponent
 from survival.components.position_component import PositionComponent
 from survival.game.enums import Direction
@@ -23,8 +25,8 @@ class LearningUtils:
         self.plot_mean_scores.append(mean_score)
 
     def plot(self):
-        display.clear_output(wait=True)
+        # display.clear_output(wait=True)
-        display.display(plt.gcf())
+        # display.display(plt.gcf())
         plt.clf()
         plt.title('Results')
         plt.xlabel('Number of Games')
@@ -35,9 +37,12 @@ class LearningUtils:
         plt.text(len(self.plot_scores) - 1, self.plot_scores[-1], str(self.plot_scores[-1]))
         plt.text(len(self.plot_mean_scores) - 1, self.plot_mean_scores[-1], str(self.plot_mean_scores[-1]))
         self.plots += 1
-        plt.savefig(f'model/plots/{self.plots}.png')
+        if MUTATE_NETWORKS:
+            plt.savefig(f'model/plots/{GeneticAlgorithm.PLOTS_COUNTER}_{self.plots}.png')
+        else:
+            plt.savefig(f'model/plots/{self.plots}.png')
         plt.show(block=False)
-        plt.pause(.1)
+        # plt.pause(.1)
 
     def append_action(self, action: Action, pos: PositionComponent):
         self.last_actions.append([action, pos.grid_position])

survival/ai/model.py

@@ -1,4 +1,5 @@
 import os
+import random
 
 import torch
 from torch import nn, optim
@@ -6,16 +7,46 @@ import torch.nn.functional as functional
 
 
 class LinearQNetwork(nn.Module):
-    def __init__(self, input_size, hidden_size, output_size, pretrained=False):
+    TORCH_ACTiVATIONS = 'tanh'
+
+    def __init__(self, nn_params, input_size, output_size, randomize=True, params=None):
         super().__init__()
-        self.linear_one = nn.Linear(input_size, hidden_size)
+        self.id = 0
-        self.linear_two = nn.Linear(hidden_size, output_size)
+        if params is None:
-        self.pretrained = pretrained
+            params = {}
+        self.params_choice = nn_params
+        self.scores = []
+        self.network_params = params
+        if randomize:
+            self.randomize()
+        self.layers = nn.ModuleList()
+        if self.network_params['layers'] == 0:
+            self.layers.append(nn.Linear(input_size, output_size))
+        else:
+            self.layers.append(nn.Linear(input_size, self.network_params['neurons']))
+
+        for i in range(self.network_params['layers'] - 1):
+            self.layers.append(nn.Linear(self.network_params['neurons'], self.network_params['neurons']))
+        if self.network_params['layers'] > 0:
+            self.ending_linear = nn.Linear(self.network_params['neurons'], output_size)
+            self.layers.append(self.ending_linear)
+
+        if self.network_params['activation'] in self.TORCH_ACTiVATIONS:
+            self.forward_func = getattr(torch, self.network_params['activation'])
+        else:
+            self.forward_func = getattr(functional, self.network_params['activation'])
+
+    def randomize(self):
+        """
+        Sets random parameters for network.
+        """
+        for key in self.params_choice:
+            self.network_params[key] = random.choice(self.params_choice[key])
 
     def forward(self, x):
-        x = functional.relu(self.linear_one(x))
+        for i in range(len(self.layers) - 1):
-        x = self.linear_two(x)
+            x = self.forward_func(self.layers[i](x))
-
+        x = self.layers[-1](x)
         return x
 
     def save(self, file_name='model.pth'):
@@ -27,24 +58,25 @@ class LinearQNetwork(nn.Module):
         torch.save(self.state_dict(), file_path)
 
     @staticmethod
-    def load(input_size, hidden_size, output_size, file_name='model.pth'):
+    def load(params, input_size, output_size, file_name='model.pth'):
         model_directory = 'model'
         file_path = os.path.join(model_directory, file_name)
         if os.path.isfile(file_path):
-            model = LinearQNetwork(input_size, hidden_size, output_size, True)
+            model = LinearQNetwork(params, input_size, output_size, True)
             model.load_state_dict(torch.load(file_path))
             model.eval()
             return model
-        return LinearQNetwork(11, 256, 3)
+        raise Exception(f'Could not find file {file_path}.')
 
 
 class QTrainer:
-    def __init__(self, model, lr, gamma):
+    def __init__(self, model, lr, gamma, optimizer):
         self.model = model
         self.lr = lr
         self.gamma = gamma
-        self.optimizer = optim.Adam(model.parameters(), lr=self.lr)
+        self.optimizer = getattr(optim, optimizer)(model.parameters(), lr=self.lr)
-        self.criterion = nn.MSELoss() # Mean squared error
+        # self.optimizer = optim.Adam(model.parameters(), lr=self.lr)
+        self.criterion = nn.MSELoss()  # Mean squared error
 
     def train_step(self, state, action, reward, next_state, done):
         state = torch.tensor(state, dtype=torch.float)

survival/ai/optimizer.py

@@ -0,0 +1,147 @@
+from functools import reduce
+from operator import add
+import random
+from typing import List
+
+from survival.ai.model import LinearQNetwork
+from survival.settings import NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE
+
+
+class Optimizer:
+    def __init__(self, params, retain=0.4,
+                 random_select=0.1, mutation_chance=0.2):
+        self.mutation_chance = mutation_chance
+        self.random_select = random_select
+        self.retain = retain
+        self.nn_params = params
+
+    def create_population(self, count: int):
+        """
+        Creates 'count' networks from random parameters.
+        :param count:
+        :return:
+        """
+        pop = []
+        for _ in range(0, count):
+            # Create a random network.
+            network = LinearQNetwork(self.nn_params, NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE)
+            # Add network to the population.
+            pop.append(network)
+
+        return pop
+
+    @staticmethod
+    def fitness(network: LinearQNetwork):
+        return sum(network.scores) / len(network.scores)
+
+    def grade(self, pop: List[LinearQNetwork]) -> float:
+        """
+        Finds average fitness for given population.
+        """
+        summed = reduce(add, (self.fitness(network) for network in pop))
+        return summed / float((len(pop)))
+
+    def breed(self, parent_one, parent_two):
+        """
+        Creates a new network from given parents.
+        :param parent_one:
+        :param parent_two:
+        :return:
+        """
+        children = []
+        for _ in range(2):
+
+            child = {}
+
+            # Loop through the parameters and pick params for the kid.
+            for param in self.nn_params:
+                child[param] = random.choice(
+                    [parent_one.network_params[param], parent_two.network_params[param]]
+                )
+
+            # Create new network object.
+            network = LinearQNetwork(self.nn_params, NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE)
+            network.network_params = child
+
+            children.append(network)
+
+        return children
+
+    def mutate(self, network: LinearQNetwork):
+        """
+        Randomly mutates one parameter of the given network.
+        :param network:
+        :return:
+        """
+        mutation = random.choice(list(self.nn_params.keys()))
+
+        # Mutate one of the params.
+        network.network_params[mutation] = random.choice(self.nn_params[mutation])
+
+        return network
+
+    def evolve(self, pop):
+        """
+        Evolves a population of networks.
+        """
+        # Get scores for each network.
+        scores = [(self.fitness(network), network) for network in pop]
+
+        # Sort the scores.
+        scores = [x[1] for x in sorted(scores, key=lambda x: x[0], reverse=True)]
+
+        # Get the number we want to keep for the next gen.
+        retain_length = int(len(scores) * self.retain)
+
+        # Keep the best networks as parents for next generation.
+        parents = scores[:retain_length]
+
+        # Keep some other networks
+        for network in scores[retain_length:]:
+            if self.random_select > random.random():
+                parents.append(network)
+
+        # Reset kept networks
+        reseted_networks = []
+        for network in parents:
+            net = LinearQNetwork(self.nn_params, NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE)
+            net.network_params = network.network_params
+            reseted_networks.append(net)
+
+        parents = reseted_networks
+
+        # Randomly mutate some of the networks.
+        for parent in parents:
+            if self.mutation_chance > random.random():
+                parent = self.mutate(parent)
+
+        # Determine the number of freed spots for the next generation.
+        parents_length = len(parents)
+        desired_length = len(pop) - parents_length
+        children = []
+
+        # Fill missing spots with new children.
+        while len(children) < desired_length:
+            # Get random parents.
+            p1 = random.randint(0, parents_length - 1)
+            p2 = random.randint(0, parents_length - 1)
+
+            # Ensure they are not the same network.
+            if p1 != p2:
+                p1 = parents[p1]
+                p2 = parents[p2]
+
+                # Breed networks.
+                babies = self.breed(p1, p2)
+
+                # Add children one at a time.
+                for baby in babies:
+                    # Don't grow larger than the desired length.
+                    if len(children) < desired_length:
+                        children.append(baby)
+
+        # parents_params = [n.network_params for n in parents]
+        # children_params = [n.network_params for n in children]
+        # parents_params.extend(children_params)
+        parents.extend(children)
+        return parents

survival/ai/test.py

@@ -0,0 +1,93 @@
+import torch
+import pygad
+from pygad.torchga import torchga
+
+
+def fitness_func(solution, sol_idx):
+    global data_inputs, data_outputs, torch_ga, model, loss_function
+
+    model_weights_dict = torchga.model_weights_as_dict(model=model,
+                                                       weights_vector=solution)
+
+    # Use the current solution as the model parameters.
+    model.load_state_dict(model_weights_dict)
+
+    predictions = model(data_inputs)
+    abs_error = loss_function(predictions, data_outputs).detach().numpy() + 0.00000001
+
+    solution_fitness = 1.0 / abs_error
+
+    return solution_fitness
+
+def callback_generation(ga_instance):
+    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
+    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
+
+# Create the PyTorch model.
+input_layer = torch.nn.Linear(3, 2)
+relu_layer = torch.nn.ReLU()
+output_layer = torch.nn.Linear(2, 1)
+
+model = torch.nn.Sequential(input_layer,
+                            relu_layer,
+                            output_layer)
+# print(model)
+
+# Create an instance of the pygad.torchga.TorchGA class to build the initial population.
+torch_ga = torchga.TorchGA(model=model,
+                           num_solutions=10)
+
+loss_function = torch.nn.L1Loss()
+
+# Data inputs
+data_inputs = torch.tensor([[0.02, 0.1, 0.15],
+                            [0.7, 0.6, 0.8],
+                            [1.5, 1.2, 1.7],
+                            [3.2, 2.9, 3.1]])
+
+# Data outputs
+data_outputs = torch.tensor([[0.1],
+                             [0.6],
+                             [1.3],
+                             [2.5]])
+
+# Prepare the PyGAD parameters. Check the documentation for more information: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html#pygad-ga-class
+num_generations = 250 # Number of generations.
+num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
+initial_population = torch_ga.population_weights # Initial population of network weights
+parent_selection_type = "sss" # Type of parent selection.
+crossover_type = "single_point" # Type of the crossover operator.
+mutation_type = "random" # Type of the mutation operator.
+mutation_percent_genes = 10 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists.
+keep_parents = -1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.
+
+ga_instance = pygad.GA(num_generations=num_generations,
+                       num_parents_mating=num_parents_mating,
+                       initial_population=initial_population,
+                       fitness_func=fitness_func,
+                       parent_selection_type=parent_selection_type,
+                       crossover_type=crossover_type,
+                       mutation_type=mutation_type,
+                       mutation_percent_genes=mutation_percent_genes,
+                       keep_parents=keep_parents,
+                       on_generation=callback_generation)
+
+ga_instance.run()
+
+# After the generations complete, some plots are showed that summarize how the outputs/fitness values evolve over generations.
+ga_instance.plot_result(title="PyGAD & PyTorch - Iteration vs. Fitness", linewidth=4)
+
+# Returning the details of the best solution.
+solution, solution_fitness, solution_idx = ga_instance.best_solution()
+print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
+print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
+
+# Fetch the parameters of the best solution.
+best_solution_weights = torchga.model_weights_as_dict(model=model,
+                                                      weights_vector=solution)
+model.load_state_dict(best_solution_weights)
+predictions = model(data_inputs)
+print("Predictions : \n", predictions.detach().numpy())
+
+abs_error = loss_function(predictions, data_outputs)
+print("Absolute Error : ", abs_error.detach().numpy())

survival/game/user_interface.py

@@ -1,6 +1,7 @@
 import pygame.font
 
-from survival import settings
+from survival.ai.genetic_algorithm import GeneticAlgorithm
+from survival.settings import MUTATE_NETWORKS, SCREEN_HEIGHT, SCREEN_WIDTH
 from survival.components.inventory_component import InventoryComponent
 from survival.generators.resource_type import ResourceType
 from survival.game.image import Image
@@ -8,8 +9,8 @@ from survival.game.image import Image
 
 class UserInterface:
     def __init__(self, window):
-        self.width = settings.SCREEN_WIDTH
+        self.width = SCREEN_WIDTH
-        self.height = settings.SCREEN_HEIGHT
+        self.height = SCREEN_HEIGHT
         self.window = window
         self.pos = (self.width - 240, 50)
         self.scale = 2
@@ -44,4 +45,3 @@ class UserInterface:
 
             textsurface = self.font.render(str(items_count), False, (255, 255, 255))
             self.window.blit(textsurface, (image.pos[0] + 48, image.pos[1] + 36))
-

survival/generators/tile_generator.py

@@ -46,7 +46,8 @@ class TileGenerator:
 
     @staticmethod
     def generate_biome_tiles(width: int, height: int):
-        seed = random.randint(1, 10)
+        # Use static seed to allow smooth learning of genetic algorithm
+        seed = 1
         octaves = 10
         file_name = f'seeds/{seed}.bin'
         biomes_file = Path(file_name)

survival/generators/world_generator.py

@@ -1,4 +1,7 @@
+from pathlib import Path
+
 from survival import esper, ResourceGenerator, PlayerGenerator
+from survival.ai.model import LinearQNetwork
 from survival.components.consumption_component import ConsumptionComponent
 from survival.components.direction_component import DirectionChangeComponent
 from survival.components.inventory_component import InventoryComponent
@@ -12,7 +15,8 @@ from survival.esper import World
 from survival.game.camera import Camera
 from survival.game.game_map import GameMap
 from survival.generators.resource_type import ResourceType
-from survival.settings import PLAYER_START_POSITION, STARTING_RESOURCES_AMOUNT, SCREEN_WIDTH, SCREEN_HEIGHT
+from survival.settings import PLAYER_START_POSITION, STARTING_RESOURCES_AMOUNT, SCREEN_WIDTH, SCREEN_HEIGHT, \
+    MUTATE_NETWORKS, NETWORK_PARAMS, NEURAL_OUTPUT_SIZE, NEURAL_INPUT_SIZE
 from survival.systems.automation_system import AutomationSystem
 from survival.systems.camera_system import CameraSystem
 from survival.systems.collision_system import CollisionSystem
@@ -42,6 +46,14 @@ class WorldGenerator:
         self.world.add_processor(MovementSystem(self.game_map), priority=20)
         self.world.add_processor(CollisionSystem(self.game_map), priority=30)
         self.world.add_processor(NeuralSystem(self.game_map, self.callback), priority=50)
+        if not MUTATE_NETWORKS:
+            model_path = Path("/model/model.pth")
+            if model_path.is_file():
+                self.world.get_processor(NeuralSystem).load_model(
+                    LinearQNetwork.load(NETWORK_PARAMS, NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE))
+            else:
+                self.world.get_processor(NeuralSystem).load_model(
+                    LinearQNetwork(NETWORK_PARAMS, NEURAL_INPUT_SIZE, NEURAL_OUTPUT_SIZE, False, NETWORK_PARAMS))
         self.world.add_processor(DrawSystem(self.camera))
         self.world.add_processor(TimeSystem())
         self.world.add_processor(AutomationSystem(self.game_map))
@@ -51,8 +63,8 @@ class WorldGenerator:
         self.world.add_processor(VisionSystem(self.camera))
 
         self.player = PlayerGenerator().create_player(self.world, self.game_map)
-        # self.world.get_processor(DrawSystem).initialize_interface(
+        self.world.get_processor(DrawSystem).initialize_interface(
-        #     self.world.component_for_entity(self.player, InventoryComponent))
+            self.world.component_for_entity(self.player, InventoryComponent))
 
         # BuildingGenerator().create_home(self.world, self.game_map)
         self.resource_generator.generate_resources(self.player)

survival/settings.py

@@ -1,7 +1,18 @@
 SCREEN_WIDTH = 1000
 SCREEN_HEIGHT = 600
-RESOURCES_AMOUNT = 100
+RESOURCES_AMOUNT = 175
 DIRECTION_CHANGE_DELAY = 5
 PLAYER_START_POSITION = [20, 10]
-STARTING_RESOURCES_AMOUNT = 10
+STARTING_RESOURCES_AMOUNT = 5
 AGENT_VISION_RANGE = 5
+NEURAL_INPUT_SIZE = 11
+NEURAL_OUTPUT_SIZE = 3
+LEARN = True
+MUTATE_NETWORKS = True
+NETWORK_PARAMS = {
+    "neurons": 256,
+    "layers": 1,
+    "activation": 'relu',
+    "ratio": 0.001,
+    "optimizer": 'Adam'
+}

survival/systems/collision_system.py

@@ -23,6 +23,7 @@ class CollisionSystem(esper.Processor):
             moving.target = tuple(map(operator.add, vector, pos.grid_position))
             moving.direction_vector = vector
             if self.check_collision(moving.target):
+                self.world.add_component(ent, ConsumeComponent(0.05))
                 self.world.remove_component(ent, MovingComponent)
                 onCol.call_all()
                 colliding_object: int = self.map.get_entity(moving.target)

survival/systems/neural_system.py

@@ -4,6 +4,7 @@ from collections import deque
 import torch
 
 from survival import esper, GameMap
+from survival.ai.genetic_algorithm import GeneticAlgorithm
 from survival.components.direction_component import DirectionChangeComponent
 from survival.components.inventory_component import InventoryComponent
 from survival.components.moving_component import MovingComponent
@@ -13,11 +14,10 @@ from survival.components.time_component import TimeComponent
 from survival.ai.graph_search import Action
 from survival.ai.learning_utils import get_state, LearningUtils
 from survival.ai.model import LinearQNetwork, QTrainer
+from survival.settings import LEARN, MUTATE_NETWORKS
 
 MAX_MEMORY = 100_000
 BATCH_SIZE = 1000
-LR = 0.001
-LEARN = False
 
 
 class NeuralSystem(esper.Processor):
@@ -25,17 +25,27 @@ class NeuralSystem(esper.Processor):
         self.game_map = game_map
         self.reset_game = callback
         self.n_games = 0  # number of games played
-        self.starting_epsilon = 100
+        if MUTATE_NETWORKS:
+            self.starting_epsilon = GeneticAlgorithm.GAMES_PER_NETWORK / 2
+        else:
+            self.starting_epsilon = 100
         self.epsilon = 0  # controlls the randomness
         self.gamma = 0.9  # discount rate
         self.memory = deque(maxlen=MAX_MEMORY)  # exceeding memory removes the left elements to make more space
-        self.model = LinearQNetwork.load(11, 256, 3)
+        self.model = None  # self.model = LinearQNetwork.load(11, 256, 3)
-        if self.model.pretrained:
+        self.trainer = None  # QTrainer(self.model, lr=LR, gamma=self.gamma)
-            self.starting_epsilon = -1
-        self.trainer = QTrainer(self.model, lr=LR, gamma=self.gamma)
         self.utils = LearningUtils()
         self.best_action = None
 
+    def load_model(self, model: LinearQNetwork):
+        self.model = model
+        self.trainer = QTrainer(self.model, self.model.network_params['ratio'], self.gamma,
+                                self.model.network_params['optimizer'])
+        self.utils = LearningUtils()
+        self.memory = deque(maxlen=MAX_MEMORY)
+        self.starting_epsilon = GeneticAlgorithm.GAMES_PER_NETWORK / 2
+        self.n_games = 0
+
     def remember(self, state, action, reward, next_state, done):
         self.memory.append((state, action, reward, next_state, done))
 
@@ -119,11 +129,13 @@ class NeuralSystem(esper.Processor):
                     self.train_long_memory()
                 if learning.score > learning.record:
                     learning.record = learning.score
-                    if LEARN:
+                    if LEARN and not MUTATE_NETWORKS:
                         self.model.save()
 
-                print('Game', self.n_games, 'Score', learning.score, 'Record', learning.record)
+                # print('Game', self.n_games, 'Score', learning.score, 'Record', learning.record)
                 self.utils.add_scores(learning, self.n_games)
+
+                self.model.scores.append(learning.score)
                 learning.score = 0
                 self.utils.plot()