├── LICENSE ├── README.md ├── classes.py ├── config_file.txt ├── fonts └── agency_fb.ttf ├── icyAI.py ├── media ├── 6.png ├── NN.png ├── TRAINING_PROCESS_avg_fitness50.png ├── TRAINING_PROCESS_speciation50.png ├── cover2.png ├── menu2.png ├── sim_speed.png ├── thumb6.png └── training.gif ├── music └── theme.mp3 ├── sprites ├── arrow.png ├── background.jpg ├── exp1.png ├── exp2.png ├── exp3.png ├── exp4.png ├── exp5.png ├── icy2.png ├── icyMan.png ├── icyMan2.png ├── icyMan3.png ├── icyMan4.png ├── icyMan5.png ├── icyMan_antagonist.png ├── pygame.png ├── wall.png └── wall2.png ├── trained_models ├── TRAINING_PROCESS │ ├── TRAINING_PROCESS10 │ ├── TRAINING_PROCESS10.pkl │ ├── TRAINING_PROCESS10.svg │ ├── TRAINING_PROCESS100 │ ├── TRAINING_PROCESS100.pkl │ ├── TRAINING_PROCESS100.svg │ ├── TRAINING_PROCESS105 │ ├── TRAINING_PROCESS105.pkl │ ├── TRAINING_PROCESS105.svg │ ├── TRAINING_PROCESS11 │ ├── TRAINING_PROCESS110 │ ├── TRAINING_PROCESS110.pkl │ ├── TRAINING_PROCESS110.svg │ ├── TRAINING_PROCESS115 │ ├── TRAINING_PROCESS115.pkl │ ├── TRAINING_PROCESS115.svg │ ├── TRAINING_PROCESS120 │ ├── TRAINING_PROCESS120.pkl │ ├── TRAINING_PROCESS120.svg │ ├── TRAINING_PROCESS125 │ ├── TRAINING_PROCESS125.pkl │ ├── TRAINING_PROCESS125.svg │ ├── TRAINING_PROCESS14 │ ├── TRAINING_PROCESS15 │ ├── TRAINING_PROCESS15.pkl │ ├── TRAINING_PROCESS15.svg │ ├── TRAINING_PROCESS17 │ ├── TRAINING_PROCESS20 │ ├── TRAINING_PROCESS20.pkl │ ├── TRAINING_PROCESS20.svg │ ├── TRAINING_PROCESS25 │ ├── TRAINING_PROCESS25.pkl │ ├── TRAINING_PROCESS25.svg │ ├── TRAINING_PROCESS30 │ ├── TRAINING_PROCESS30.pkl │ ├── TRAINING_PROCESS30.svg │ ├── TRAINING_PROCESS35 │ ├── TRAINING_PROCESS35.pkl │ ├── TRAINING_PROCESS35.svg │ ├── TRAINING_PROCESS40 │ ├── TRAINING_PROCESS40.pkl │ ├── TRAINING_PROCESS45 │ ├── TRAINING_PROCESS45.pkl │ ├── TRAINING_PROCESS45.svg │ ├── TRAINING_PROCESS5 │ ├── TRAINING_PROCESS5.pkl │ ├── TRAINING_PROCESS5.svg │ ├── TRAINING_PROCESS50 │ ├── TRAINING_PROCESS50.pkl │ ├── TRAINING_PROCESS50.svg │ ├── TRAINING_PROCESS55 │ ├── TRAINING_PROCESS55.pkl │ ├── TRAINING_PROCESS55.svg │ ├── TRAINING_PROCESS6 │ ├── TRAINING_PROCESS60 │ ├── TRAINING_PROCESS60.pkl │ ├── TRAINING_PROCESS60.svg │ ├── TRAINING_PROCESS65 │ ├── TRAINING_PROCESS65.pkl │ ├── TRAINING_PROCESS65.svg │ ├── TRAINING_PROCESS70 │ ├── TRAINING_PROCESS70.pkl │ ├── TRAINING_PROCESS70.svg │ ├── TRAINING_PROCESS75 │ ├── TRAINING_PROCESS75.pkl │ ├── TRAINING_PROCESS75.svg │ ├── TRAINING_PROCESS80 │ ├── TRAINING_PROCESS80.pkl │ ├── TRAINING_PROCESS80.svg │ ├── TRAINING_PROCESS85 │ ├── TRAINING_PROCESS85.pkl │ ├── TRAINING_PROCESS85.svg │ ├── TRAINING_PROCESS90 │ ├── TRAINING_PROCESS90.pkl │ ├── TRAINING_PROCESS90.svg │ ├── TRAINING_PROCESS95 │ ├── TRAINING_PROCESS95.pkl │ ├── TRAINING_PROCESS95.svg │ ├── TRAINING_PROCESS_avg_fitness10.png │ ├── TRAINING_PROCESS_avg_fitness100.png │ ├── TRAINING_PROCESS_avg_fitness105.png │ ├── TRAINING_PROCESS_avg_fitness110.png │ ├── TRAINING_PROCESS_avg_fitness115.png │ ├── TRAINING_PROCESS_avg_fitness120.png │ ├── TRAINING_PROCESS_avg_fitness125.png │ ├── TRAINING_PROCESS_avg_fitness15.png │ ├── TRAINING_PROCESS_avg_fitness20.png │ ├── TRAINING_PROCESS_avg_fitness25.png │ ├── TRAINING_PROCESS_avg_fitness30.png │ ├── TRAINING_PROCESS_avg_fitness35.png │ ├── TRAINING_PROCESS_avg_fitness40.png │ ├── TRAINING_PROCESS_avg_fitness45.png │ ├── TRAINING_PROCESS_avg_fitness5.png │ ├── TRAINING_PROCESS_avg_fitness50.png │ ├── TRAINING_PROCESS_avg_fitness55.png │ ├── TRAINING_PROCESS_avg_fitness60.png │ ├── TRAINING_PROCESS_avg_fitness65.png │ ├── TRAINING_PROCESS_avg_fitness70.png │ ├── TRAINING_PROCESS_avg_fitness75.png │ ├── TRAINING_PROCESS_avg_fitness80.png │ ├── TRAINING_PROCESS_avg_fitness85.png │ ├── TRAINING_PROCESS_avg_fitness90.png │ ├── TRAINING_PROCESS_avg_fitness95.png │ ├── TRAINING_PROCESS_speciation10.png │ ├── TRAINING_PROCESS_speciation100.png │ ├── TRAINING_PROCESS_speciation105.png │ ├── TRAINING_PROCESS_speciation110.png │ ├── TRAINING_PROCESS_speciation115.png │ ├── TRAINING_PROCESS_speciation120.png │ ├── TRAINING_PROCESS_speciation125.png │ ├── TRAINING_PROCESS_speciation15.png │ ├── TRAINING_PROCESS_speciation20.png │ ├── TRAINING_PROCESS_speciation25.png │ ├── TRAINING_PROCESS_speciation30.png │ ├── TRAINING_PROCESS_speciation35.png │ ├── TRAINING_PROCESS_speciation40.png │ ├── TRAINING_PROCESS_speciation45.png │ ├── TRAINING_PROCESS_speciation50.png │ ├── TRAINING_PROCESS_speciation55.png │ ├── TRAINING_PROCESS_speciation60.png │ ├── TRAINING_PROCESS_speciation65.png │ ├── TRAINING_PROCESS_speciation70.png │ ├── TRAINING_PROCESS_speciation75.png │ ├── TRAINING_PROCESS_speciation80.png │ ├── TRAINING_PROCESS_speciation85.png │ ├── TRAINING_PROCESS_speciation90.png │ └── TRAINING_PROCESS_speciation95.png └── model1 │ ├── model1_4 │ ├── model1_5 │ ├── model1_5.pkl │ ├── model1_5.svg │ ├── model1_avg_fitness5.png │ ├── model1_config.txt │ └── model1_speciation5.png └── visualize.py /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 nikp06 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## About IcyAI 2 | 3 | Hello Internet! Welcome to *icyAI* - a project for which I recreated the game Icy Tower in Python with Pygame and built an AI that learns how to play it - here you can do so to. 4 | 5 | [![Thumbnail](media/cover2.png)](https://youtu.be/W6qyRbmr_aA) 6 | 7 | Click [here](https://youtu.be/W6qyRbmr_aA), to see what I did in this project on YouTube. 8 | 9 | Download the full project on [itch.io](https://nikp06.itch.io/icyai-icy-tower-ai-vs-human) (only Windows for now/I would appreciate someone making a mac or linux build and sending it to me :P -> I used pyinstaller on my windows machine). 10 | 11 | I made use of a genetic algorithm called NEAT. NEAT evolves neural network topologies through neuroevolution. 12 | It is a known method from the domain of reinforcement learning. The concept is further explained in the video. You can also read the initial [NEAT paper](http://nn.cs.utexas.edu/downloads/papers/stanley.cec02.pdf) or browse through the [NEAT documentation](https://neat-python.readthedocs.io/en/latest/neat_overview.html). 13 | This repository contains all files needed to train the AI for yourself. 14 | 15 | ![AI during training](media/training.gif) 16 | 17 | ## Description 18 | 19 | This repository contains everything you need to play the game for yourself or to train your own Icy Tower AI. 20 | Feel free to play around with the configuration file. Maybe you'll find a way to make the AI learn even more complex behavior. I'd be curious to know about it in case you do. 21 | 22 | ## How to use 23 | 24 | 1. For starting the game: 25 | ``` 26 | py icyAI.py 27 | ``` 28 | 2. Choose a screen-size (large is recommended, others might change physics of the game) 29 | 30 | 3. Simply navigate through the menu: 31 | * PLAY - play for yourself 32 | * TRAIN AI - train a new AI and specify how many generations 33 | * LET AI PLAY - choose a trained model and let the AI play and specify how many runs 34 | * HUMAN VS. AI - choose a trained model and play against this trained AI 35 | ![menu](media/menu2.png) 36 | 37 | 4. Enjoy the game and music, play around with the configuration file, experiment with parameters, analyze the statistics, speed up the simulation 38 | 39 | Fitness Stats | Speciation Stats | Neural Network 40 | :-------------------------:|:-------------------------:|:-------------------------: 41 | ![](media/TRAINING_PROCESS_avg_fitness50.png) | ![](media/TRAINING_PROCESS_speciation50.png) | ![](media/NN.png) 42 | 43 | ## Requirements and modules 44 | 45 | - python 3 46 | - pygame 47 | - pickle 48 | - neat 49 | - sys 50 | - os 51 | - numpy 52 | - tkinter 53 | - random 54 | - glob 55 | - visualize 56 | - re 57 | - shutil 58 | - time 59 | -------------------------------------------------------------------------------- /config_file.txt: -------------------------------------------------------------------------------- 1 | [NEAT] 2 | fitness_criterion = mean 3 | fitness_threshold = 1000000 4 | pop_size = 500 5 | reset_on_extinction = True 6 | 7 | [DefaultGenome] 8 | # node activation options 9 | activation_default = tanh 10 | activation_mutate_rate = 0.0 11 | activation_options = tanh 12 | 13 | # node aggregation options 14 | aggregation_default = sum 15 | aggregation_mutate_rate = 0.0 16 | aggregation_options = sum 17 | 18 | # node bias options 19 | bias_init_mean = 0.0 20 | bias_init_stdev = 1.0 21 | bias_max_value = 30.0 22 | bias_min_value = -30.0 23 | bias_mutate_power = 0.5 24 | bias_mutate_rate = 0.7 25 | bias_replace_rate = 0.1 26 | 27 | # genome compatibility options 28 | compatibility_disjoint_coefficient = 1.0 29 | compatibility_weight_coefficient = 0.5 30 | 31 | # connection add/remove rates 32 | conn_add_prob = 0.5 33 | conn_delete_prob = 0.3 34 | 35 | # connection enable options 36 | enabled_default = True 37 | enabled_mutate_rate = 0.01 38 | 39 | feed_forward = False 40 | initial_connection = unconnected 41 | 42 | # node add/remove rates 43 | node_add_prob = 0.03 44 | node_delete_prob = 0.025 45 | 46 | # network parameters 47 | num_hidden = 0 48 | num_inputs = 38 49 | num_outputs = 3 50 | 51 | # node response options 52 | response_init_mean = 1.0 53 | response_init_stdev = 0.0 54 | response_max_value = 30.0 55 | response_min_value = -30.0 56 | response_mutate_power = 0.1 57 | response_mutate_rate = 0.1 58 | response_replace_rate = 0.1 59 | 60 | # connection weight options 61 | weight_init_mean = 0.0 62 | weight_init_stdev = 1.0 63 | weight_max_value = 30 64 | weight_min_value = -30 65 | weight_mutate_power = 0.5 66 | weight_mutate_rate = 0.8 67 | weight_replace_rate = 0.1 68 | 69 | [DefaultSpeciesSet] 70 | compatibility_threshold = 3.0 71 | 72 | [DefaultStagnation] 73 | species_fitness_func = max 74 | max_stagnation = 20 75 | species_elitism = 2 76 | 77 | [DefaultReproduction] 78 | elitism = 2 79 | survival_threshold = 0.2 80 | min_species_size = 2 -------------------------------------------------------------------------------- /fonts/agency_fb.ttf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nikp06/icyAI/20a445aef645def2908b36895521340c4031902f/fonts/agency_fb.ttf -------------------------------------------------------------------------------- /icyAI.py: -------------------------------------------------------------------------------- 1 | import pickle 2 | import neat 3 | import os 4 | import glob 5 | # import visualize 6 | import re 7 | import shutil 8 | import time 9 | from classes import IcyTowerGame, menu, screen_options, update_variables, specify_amount 10 | 11 | GENERATION = 0 12 | CLOCK_SPEED = 60 13 | DRAW = True 14 | RECORDING = False 15 | RECORDING_COUNTER = 0 16 | PLAY = False 17 | PLAY_AI = False 18 | TRAIN_AI = False 19 | VERSUS = False 20 | 21 | 22 | def main(genomes, config): 23 | global GENERATION 24 | global P 25 | global CLOCK_SPEED 26 | global DRAW 27 | global RECORDING_COUNTER 28 | global PLAY 29 | global PLAY_AI 30 | global TRAIN_AI 31 | global VERSUS 32 | 33 | game = IcyTowerGame(genomes, config, train=TRAIN_AI, ai=True if TRAIN_AI or PLAY_AI or VERSUS else False, versus=VERSUS) 34 | 35 | game.generation = GENERATION 36 | if not DRAW: 37 | game.draw = False 38 | 39 | # if len(sys.argv) > 1: 40 | if TRAIN_AI: 41 | i = None 42 | for _, g in game.genomes: 43 | if i != P.species.get_species_id(g.key): 44 | i = P.species.get_species_id(g.key) 45 | g.species_id = i 46 | game.color_species() 47 | game.clock_speed = CLOCK_SPEED 48 | 49 | while True: 50 | game.play_step() 51 | # if not RECORDING and GENERATION <= 20: 52 | # if not game.draw: 53 | # game.clock_speed = 60 54 | # game.draw = True 55 | # # start_stop_capture() 56 | # # RECORDING_COUNTER += 1 57 | # elif not RECORDING and GENERATION % 10 == 0: 58 | # if not game.draw: 59 | # game.clock_speed = 60 60 | # game.draw = True 61 | # # start_stop_capture() 62 | # # RECORDING_COUNTER += 1 63 | # if not RECORDING: 64 | # if game.highest_fitness > 6800: 65 | # if not game.draw: 66 | # game.clock_speed = 60 67 | # game.draw = True 68 | # # start_stop_capture() 69 | # # RECORDING_COUNTER += 1 70 | 71 | # if len(game.players) == 1: 72 | # last_player = game.players[0] 73 | # last_genome = game.genomes[0] 74 | if len(game.players) == 0: # or game.ai_players[0].rect.y > 900 or game.human_players[0].rect.y > 900: 75 | GENERATION += 1 76 | if RECORDING: 77 | # start_stop_capture() 78 | game.clock_speed = CLOCK_SPEED 79 | game.draw_window_pause() 80 | else: 81 | DRAW = game.draw 82 | CLOCK_SPEED = game.clock_speed 83 | break 84 | 85 | if game.versus: 86 | if game.human_players[0].rect.y > screen_size - game.human_players[0].rect.height or game.ai_players[0].rect.y > screen_size - game.ai_players[0].rect.height: 87 | break 88 | 89 | if genomes is None: 90 | if game.human_players[0].rect.y > screen_size - game.human_players[0].rect.height: 91 | break 92 | 93 | if PLAY_AI: 94 | if game.ai_players[0].rect.y > screen_size - game.ai_players[0].rect.height: 95 | break 96 | 97 | 98 | def extract_number(f): 99 | s = re.findall("\d+$", f) 100 | return int(s[0]) if s else -1, f 101 | 102 | 103 | # def start_stop_capture(): 104 | # # for automated screen capturing on windows 105 | # global RECORDING 106 | # keyboard = Controller() 107 | # keyboard.press(Key.cmd) 108 | # keyboard.press(Key.alt) 109 | # keyboard.press('r') 110 | # time.sleep(2) 111 | # keyboard.release('r') 112 | # keyboard.release(Key.alt) 113 | # keyboard.release(Key.cmd) 114 | # time.sleep(2) 115 | # RECORDING = not RECORDING 116 | 117 | 118 | def run(config_path, open_file, play_ai, train_ai, versus, runs): 119 | global GENERATION 120 | global P 121 | config = neat.config.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, 122 | neat.DefaultStagnation, config_path) 123 | if train_ai or play_ai or versus: 124 | if train_ai: 125 | # checkpointer = neat.Checkpointer(int(sys.argv[3])) 126 | checkpointer = neat.Checkpointer(5) 127 | model_list = [x[0] for x in os.walk('trained_models')] 128 | if len(model_list) == 1: 129 | os.mkdir(os.path.join('trained_models', 'model1')) 130 | model_name = os.path.join('trained_models', 'model1') 131 | # model_path = os.path.join('trained_models', 'model1') 132 | else: 133 | model_nr = int(max(model_list, key=extract_number)[20:]) + 1 134 | model_name = max(model_list, key=extract_number)[:20]+str(model_nr) 135 | os.mkdir(model_name) 136 | print(f"\nCreating new model '{model_name}'...") 137 | P = neat.Population(config) 138 | checkpointer.filename_prefix = os.path.join(model_name, model_name[15:] + '_') 139 | 140 | if play_ai or versus: 141 | # Load specified model 142 | # path = os.path.join('trained_models', sys.argv[1]) 143 | # if os.path.isdir(path): 144 | 145 | if len(glob.glob(os.path.join(open_file, open_file.split('/')[-1] + '_*.pkl'))) != 0: 146 | print("jo") 147 | # if os.path.isfile(os.path.join(sys.argv[1], sys.argv[1]+'*'+'.pkl')): 148 | filenames_list = [] 149 | for item in glob.glob(os.path.join(open_file, open_file.split('/')[-1] + '_*')): 150 | print(item) 151 | if '.' not in item: 152 | filenames_list.append(item) 153 | try: 154 | P = neat.Checkpointer.restore_checkpoint(max(filenames_list, key=extract_number)) 155 | except: 156 | filenames_list.remove(max(filenames_list, key=extract_number)) 157 | P = neat.Checkpointer.restore_checkpoint(max(filenames_list, key=extract_number)) 158 | 159 | print(f"\nLoading existing model '{max(filenames_list, key=extract_number)}'...") 160 | filenames_list = [_[:-4] for _ in os.listdir(open_file) if _.endswith('.pkl')] 161 | # print(max(filenames_list, key=extract_number)) 162 | with open(os.path.join(open_file, max(filenames_list, key=extract_number) + '.pkl'), "rb") as f: 163 | genome = pickle.load(f) 164 | 165 | P.add_reporter(neat.StdOutReporter(True)) 166 | stats = neat.StatisticsReporter() 167 | # print(P.best_genome) 168 | # print(stats.best_genome().fitness) 169 | # print(P.) 170 | P.add_reporter(stats) 171 | if train_ai: 172 | P.add_reporter(checkpointer) 173 | # print(P.species) 174 | # print(P.population) 175 | GENERATION = P.generation 176 | # print(genome.key) 177 | # print(P.species.get_species(genome.key).members) 178 | n = runs 179 | # print(P.species) 180 | if train_ai: 181 | 182 | node_names = {-1: 'A', -2: 'B', 0: 'A XOR B'} 183 | shutil.copyfile('config_file.txt', os.path.join(model_name, model_name[15:] + '_config.txt')) 184 | 185 | # run the algorithm the specified amount of times x 25 -> after each iteration the best genome is saved 186 | for i in range(round(n/5)): 187 | print(f"\nTraining in process for given model '{model_name}' for {n} runs...") 188 | winner = P.run(main, 5) 189 | 190 | # visualize.draw_net(config, winner, False, filename=os.path.join(model_name, model_name[15:] + '_' + str(GENERATION)), 191 | # node_names=node_names) 192 | # visualize.plot_stats(stats, ylog=False, view=False, 193 | # filename=os.path.join(model_name, model_name[15:] + '_avg_fitness' + str(GENERATION))) 194 | # visualize.plot_species(stats, view=False, 195 | # filename=os.path.join(model_name, model_name[15:] + '_speciation' + str(GENERATION))) 196 | # checkpointer.save_checkpoint(config, P.population, P.species, P.generation) 197 | with open(os.path.join(model_name, model_name[15:] + '_' + str(GENERATION) + '.pkl'), "wb") as f: 198 | pickle.dump(winner, f) 199 | f.close() 200 | 201 | elif play_ai or versus: 202 | # Convert loaded genome into required data structure 203 | genomes = [(1, genome)] # genome.key instead of 1? 204 | 205 | print(f"\nPlaying in process for given model '{open_file}' for {n} runs...") 206 | for i in range(n): 207 | main(genomes, config) 208 | GENERATION -= 1 209 | 210 | else: 211 | # for i in range(runs): 212 | main(genomes=None, config=None) 213 | # print("\nNo/Invalid Arguments given... Please specify {name of model}, {train/play}, {n_runs}") 214 | start_new() 215 | 216 | 217 | def start_new(): 218 | open_file, PLAY, PLAY_AI, TRAIN_AI, VERSUS = menu() 219 | runs = 0 220 | if not PLAY: 221 | runs = specify_amount() 222 | run(config_path, open_file, PLAY_AI, TRAIN_AI, VERSUS, runs) 223 | 224 | 225 | if __name__ == '__main__': 226 | screen_size = screen_options() 227 | update_variables(screen_size) 228 | start_time = time.time() 229 | local_dir = os.path.dirname(__name__) 230 | config_path = os.path.join(local_dir, "config_file.txt") 231 | open_file, PLAY, PLAY_AI, TRAIN_AI, VERSUS = menu() 232 | runs = 0 233 | if not PLAY: 234 | runs = specify_amount() 235 | run(config_path, open_file, PLAY_AI, TRAIN_AI, VERSUS, runs) 236 | print("--- %s minutes ---" % (round(time.time() - start_time)/60), 2) 237 | -------------------------------------------------------------------------------- /media/6.png: 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-------------------------------------------------------------------------------- /trained_models/model1/model1_avg_fitness5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nikp06/icyAI/20a445aef645def2908b36895521340c4031902f/trained_models/model1/model1_avg_fitness5.png -------------------------------------------------------------------------------- /trained_models/model1/model1_config.txt: -------------------------------------------------------------------------------- 1 | [NEAT] 2 | fitness_criterion = mean 3 | fitness_threshold = 1000000 4 | pop_size = 500 5 | reset_on_extinction = True 6 | 7 | [DefaultGenome] 8 | # node activation options 9 | activation_default = tanh 10 | activation_mutate_rate = 0.0 11 | activation_options = tanh 12 | 13 | # node aggregation options 14 | aggregation_default = sum 15 | aggregation_mutate_rate = 0.0 16 | aggregation_options = sum 17 | 18 | # node bias options 19 | bias_init_mean = 0.0 20 | bias_init_stdev = 1.0 21 | bias_max_value = 30.0 22 | bias_min_value = -30.0 23 | bias_mutate_power = 0.5 24 | bias_mutate_rate = 0.7 25 | bias_replace_rate = 0.1 26 | 27 | # genome compatibility options 28 | compatibility_disjoint_coefficient = 1.0 29 | compatibility_weight_coefficient = 0.5 30 | 31 | # connection add/remove rates 32 | conn_add_prob = 0.5 33 | conn_delete_prob = 0.3 34 | 35 | # connection enable options 36 | enabled_default = True 37 | enabled_mutate_rate = 0.01 38 | 39 | feed_forward = False 40 | initial_connection = unconnected 41 | 42 | # node add/remove rates 43 | node_add_prob = 0.03 44 | node_delete_prob = 0.025 45 | 46 | # network parameters 47 | num_hidden = 0 48 | num_inputs = 38 49 | num_outputs = 3 50 | 51 | # node response options 52 | response_init_mean = 1.0 53 | response_init_stdev = 0.0 54 | response_max_value = 30.0 55 | response_min_value = -30.0 56 | response_mutate_power = 0.1 57 | response_mutate_rate = 0.1 58 | response_replace_rate = 0.1 59 | 60 | # connection weight options 61 | weight_init_mean = 0.0 62 | weight_init_stdev = 1.0 63 | weight_max_value = 30 64 | weight_min_value = -30 65 | weight_mutate_power = 0.5 66 | weight_mutate_rate = 0.8 67 | weight_replace_rate = 0.1 68 | 69 | [DefaultSpeciesSet] 70 | compatibility_threshold = 3.0 71 | 72 | [DefaultStagnation] 73 | species_fitness_func = max 74 | max_stagnation = 20 75 | species_elitism = 2 76 | 77 | [DefaultReproduction] 78 | elitism = 2 79 | survival_threshold = 0.2 80 | min_species_size = 2 -------------------------------------------------------------------------------- /trained_models/model1/model1_speciation5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nikp06/icyAI/20a445aef645def2908b36895521340c4031902f/trained_models/model1/model1_speciation5.png -------------------------------------------------------------------------------- /visualize.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | 3 | import copy 4 | import warnings 5 | 6 | import graphviz 7 | import matplotlib.pyplot as plt 8 | import numpy as np 9 | 10 | 11 | def plot_stats(statistics, ylog=False, view=False, filename='avg_fitness.svg'): 12 | """ Plots the population's average and best fitness. """ 13 | if plt is None: 14 | warnings.warn("This display is not available due to a missing optional dependency (matplotlib)") 15 | return 16 | 17 | generation = range(len(statistics.most_fit_genomes)) 18 | best_fitness = [c.fitness for c in statistics.most_fit_genomes] 19 | avg_fitness = np.array(statistics.get_fitness_mean()) 20 | stdev_fitness = np.array(statistics.get_fitness_stdev()) 21 | 22 | plt.plot(generation, avg_fitness, 'b-', label="average") 23 | plt.plot(generation, avg_fitness - stdev_fitness, 'g-.', label="-1 sd") 24 | plt.plot(generation, avg_fitness + stdev_fitness, 'g-.', label="+1 sd") 25 | plt.plot(generation, best_fitness, 'r-', label="best") 26 | 27 | plt.title("Population's average and best fitness") 28 | plt.xlabel("Generations") 29 | plt.ylabel("Fitness") 30 | plt.grid() 31 | plt.legend(loc="best") 32 | if ylog: 33 | plt.gca().set_yscale('symlog') 34 | 35 | plt.savefig(filename) 36 | if view: 37 | plt.show() 38 | 39 | plt.close() 40 | 41 | 42 | def plot_spikes(spikes, view=False, filename=None, title=None): 43 | """ Plots the trains for a single spiking neuron. """ 44 | t_values = [t for t, I, v, u, f in spikes] 45 | v_values = [v for t, I, v, u, f in spikes] 46 | u_values = [u for t, I, v, u, f in spikes] 47 | I_values = [I for t, I, v, u, f in spikes] 48 | f_values = [f for t, I, v, u, f in spikes] 49 | 50 | fig = plt.figure() 51 | plt.subplot(4, 1, 1) 52 | plt.ylabel("Potential (mv)") 53 | plt.xlabel("Time (in ms)") 54 | plt.grid() 55 | plt.plot(t_values, v_values, "g-") 56 | 57 | if title is None: 58 | plt.title("Izhikevich's spiking neuron model") 59 | else: 60 | plt.title("Izhikevich's spiking neuron model ({0!s})".format(title)) 61 | 62 | plt.subplot(4, 1, 2) 63 | plt.ylabel("Fired") 64 | plt.xlabel("Time (in ms)") 65 | plt.grid() 66 | plt.plot(t_values, f_values, "r-") 67 | 68 | plt.subplot(4, 1, 3) 69 | plt.ylabel("Recovery (u)") 70 | plt.xlabel("Time (in ms)") 71 | plt.grid() 72 | plt.plot(t_values, u_values, "r-") 73 | 74 | plt.subplot(4, 1, 4) 75 | plt.ylabel("Current (I)") 76 | plt.xlabel("Time (in ms)") 77 | plt.grid() 78 | plt.plot(t_values, I_values, "r-o") 79 | 80 | if filename is not None: 81 | plt.savefig(filename) 82 | 83 | if view: 84 | plt.show() 85 | plt.close() 86 | fig = None 87 | 88 | return fig 89 | 90 | 91 | def plot_species(statistics, view=False, filename='speciation.svg'): 92 | """ Visualizes speciation throughout evolution. """ 93 | if plt is None: 94 | warnings.warn("This display is not available due to a missing optional dependency (matplotlib)") 95 | return 96 | 97 | species_sizes = statistics.get_species_sizes() 98 | num_generations = len(species_sizes) 99 | curves = np.array(species_sizes).T 100 | 101 | fig, ax = plt.subplots() 102 | ax.stackplot(range(num_generations), *curves) 103 | 104 | plt.title("Speciation") 105 | plt.ylabel("Size per Species") 106 | plt.xlabel("Generations") 107 | 108 | plt.savefig(filename) 109 | 110 | if view: 111 | plt.show() 112 | 113 | plt.close() 114 | 115 | 116 | def draw_net(config, genome, view=False, filename=None, node_names=None, show_disabled=True, prune_unused=False, 117 | node_colors=None, fmt='svg'): 118 | """ Receives a genome and draws a neural network with arbitrary topology. """ 119 | # Attributes for network nodes. 120 | if graphviz is None: 121 | warnings.warn("This display is not available due to a missing optional dependency (graphviz)") 122 | return 123 | 124 | if node_names is None: 125 | node_names = {} 126 | 127 | assert type(node_names) is dict 128 | 129 | if node_colors is None: 130 | node_colors = {} 131 | 132 | assert type(node_colors) is dict 133 | 134 | node_attrs = { 135 | 'shape': 'circle', 136 | 'fontsize': '7', 137 | 'height': '0.15', 138 | 'width': '0.15'} 139 | 140 | dot = graphviz.Digraph(format=fmt, node_attr=node_attrs) 141 | 142 | inputs = set() 143 | for k in config.genome_config.input_keys: 144 | inputs.add(k) 145 | name = node_names.get(k, str(k)) 146 | input_attrs = {'style': 'filled', 'shape': 'box', 'fillcolor': node_colors.get(k, 'lightgray')} 147 | dot.node(name, _attributes=input_attrs) 148 | 149 | outputs = set() 150 | for k in config.genome_config.output_keys: 151 | outputs.add(k) 152 | name = node_names.get(k, str(k)) 153 | node_attrs = {'style': 'filled', 'fillcolor': node_colors.get(k, 'lightblue')} 154 | 155 | dot.node(name, _attributes=node_attrs) 156 | 157 | if prune_unused: 158 | connections = set() 159 | for cg in genome.connections.values(): 160 | if cg.enabled or show_disabled: 161 | connections.add((cg.in_node_id, cg.out_node_id)) 162 | 163 | used_nodes = copy.copy(outputs) 164 | pending = copy.copy(outputs) 165 | while pending: 166 | new_pending = set() 167 | for a, b in connections: 168 | if b in pending and a not in used_nodes: 169 | new_pending.add(a) 170 | used_nodes.add(a) 171 | pending = new_pending 172 | else: 173 | used_nodes = set(genome.nodes.keys()) 174 | 175 | for n in used_nodes: 176 | if n in inputs or n in outputs: 177 | continue 178 | 179 | attrs = {'style': 'filled', 180 | 'fillcolor': node_colors.get(n, 'white')} 181 | dot.node(str(n), _attributes=attrs) 182 | 183 | for cg in genome.connections.values(): 184 | if cg.enabled or show_disabled: 185 | #if cg.input not in used_nodes or cg.output not in used_nodes: 186 | # continue 187 | input, output = cg.key 188 | a = node_names.get(input, str(input)) 189 | b = node_names.get(output, str(output)) 190 | style = 'solid' if cg.enabled else 'dotted' 191 | color = 'green' if cg.weight > 0 else 'red' 192 | width = str(0.1 + abs(cg.weight / 5.0)) 193 | dot.edge(a, b, _attributes={'style': style, 'color': color, 'penwidth': width}) 194 | 195 | dot.render(filename, view=view) 196 | return dot 197 | --------------------------------------------------------------------------------