From d9c2c819c8a151efbf5d52852585feea9dde6cf0 Mon Sep 17 00:00:00 2001 From: m-sergent <35770667+m-sergent@users.noreply.github.com> Date: Sun, 11 Mar 2018 22:53:41 +0100 Subject: [PATCH 1/3] Add files via upload --- Sergent/FlappyAgent.py | 34 ++++++ Sergent/comments.txt | 57 +++++++++ Sergent/run.py | 30 +++++ Sergent/screeny_birb.dqf | Bin 0 -> 6479416 bytes Sergent/screeny_birb.py | 247 +++++++++++++++++++++++++++++++++++++++ 5 files changed, 368 insertions(+) create mode 100644 Sergent/FlappyAgent.py create mode 100644 Sergent/comments.txt create mode 100644 Sergent/run.py create mode 100644 Sergent/screeny_birb.dqf create mode 100644 Sergent/screeny_birb.py diff --git a/Sergent/FlappyAgent.py b/Sergent/FlappyAgent.py new file mode 100644 index 0000000..82efa0c --- /dev/null +++ b/Sergent/FlappyAgent.py @@ -0,0 +1,34 @@ +import numpy as np +from keras.models import load_model +from collections import deque +from skimage import transform,color +from ple.games.flappybird import FlappyBird +from ple import PLE + +def ProcessScreen(x): + return 255*transform.resize(color.rgb2gray(x[60:, 25:310,:]),(80,80)) + + +birb = load_model('screeny_birb.dqf') +game = FlappyBird() +p = PLE(game, fps=30, frame_skip=1, num_steps=1) +list_actions = p.getActionSet() +img_dims = (80,80) + +frameDeque = deque([np.zeros(img_dims),np.zeros(img_dims),np.zeros(img_dims),np.zeros(img_dims)], maxlen=4) + +def FlappyPolicy(state, screen): + global birb + global frameDeque + global list_actions + + x = ProcessScreen(screen) + # Reinitialize the deque if we start a new game + if not np.any(x[10:,:]): # if everything in front of Flappy is black + frameDeque = deque([np.zeros(img_dims),np.zeros(img_dims),np.zeros(img_dims),np.zeros(img_dims)], maxlen=4) + + frameDeque.append(x) + frameStack = np.stack(frameDeque, axis=-1) + a = list_actions[np.argmax(birb.predict(np.expand_dims(frameStack,axis=0)))] + + return a \ No newline at end of file diff --git a/Sergent/comments.txt b/Sergent/comments.txt new file mode 100644 index 0000000..8326d4b --- /dev/null +++ b/Sergent/comments.txt @@ -0,0 +1,57 @@ +COMMENTAIRES SUR LE PROJET "MAKE FLAPPY FLAP AGAIN" + + Etant données les règles du projet, j'ai tout de suite choisi de chercher +une méthode d'apprentissage sans feature engineering, soit sur le vecteur d'état, +soit sur les images. Comme l'avenir me l'a enseigné, un algorithme SARSA ou +Q-learning était long à mettre en place, notamment à cause du travail de feature +engineering ; de plus, les résultats étaient mitigés par rapport aux méthodes de +deep learning (par exemple, le fait de "découper" l'axe y en 15 pour réduire le +nombre d'états entraîne des collisions inévitables si l'oiseau et le tuyau suivant +sont dans certaines conditions) ; enfin, ce n'était vraiment pas cher payé pour +tout le travail que ça représentait ! Quand je pense que certains se sont échinés +à peaufiner leur traitement des features pour tenter d'améliorer un peu leurs +performances, sans vraiment comprendre ce qui marchait et ce qui allait au +contraire dégrader leurs résultats... J'espère que tout ce travail ne sera pas +sanctionné par un maigre 10/20. Cela dit, cette méthode avait l'avantage d'être +docile, l'algorithme n'en faisait pas à sa guise et convergeait rapidement du +moment que le feature engineering était correct. Elle constituait donc une bonne +solution de repli. + + Quant à moi, j'ai donc commencé par tenter de mettre en place un DQN sur le +vecteur d'état en m'inspirant fortement du notebook sur le morpion ; mais rien +n'y a fait, peu importe les paramètres que je modifiais (pertinemment à mon sens), +le bougre n'a pas voulu converger. Mes condisciples qui avaient choisi comme moi +cette méthode, se sont soit résignés à se rabattre sur le Q-learning ou le SARSA, +soit ont attaqué le deep learning sur les frames du jeu. Cette deuxième option +semblait plus ardue encore, du "DeepMind level" comparé à du simple "good job" ! +Quelle ne fut pas ma surprise quand ceux qui l'avaient tentée me dirent : +"C'est trop simple, ça marche super bien." Tel un mouton de Panurge, j'ai donc moi +aussi adapté le notebook sur Breakout à Flappy Bird. + + Passons sous silence les erreurs idiotes de programmation ne relevant pas du +Machine Learning qui ont ralenti ma progression : notons tout de même qu'à un moment, +mon oiseau ne flappait pas du tout, ce qui m'était tout à fait incompréhensible, +jusqu'à ce que je me rende compte que la liste des actions n'était pas [0, 1] mais +[119, None]. + + Ce qui m'a rendu le plus circonspect, c'est la fonction process_screen du +notebook, et les deux fonctions de skimage qu'elle utilisait, rgb2gray et cette +satanée resize. Sans utiliser process_screen, j'ai voulu utiliser resize directement, +en la testant dans un notebook : pas moyen d'obtenir une image correctement +interpolée... Je me suis arraché les cheveux à vouloir faire marcher cette fonction, +puis j'ai trouvé une fonction imresize dans scipy.misc qui redimensionne une image +en conservant son ratio. J'ai donc passé des images rectangulaires en entrée du DQN. +Et les résultats furent catastrophiques. Puis Ilyass m'a dit, "keras n'aime que les +arrays carrés en entrée, faut que ce soit carré, et puis ça marche." J'ai donc copié +la fonction process_screen, qui a fonctionné (je ne sais toujours pas pourquoi, peut- +être est-ce lié au type de stockage des pixels, sur un ou deux octets ?). +Et ça a marché. + +En guise de conclusion, voici le sentiment principal qui m'habite après la réalisation +du DQN sur les images : j'ai l'impression qu'il n'y avait qu'une seule bonne solution, +et c'était de coller au plus près de l'architecture proposée par DeepMind. Toute +déviance, toute incartade, toute tentative d'amélioration ou du moins d'adaptation de +cette architecture au problème Flappy Bird semblait soldée par des résultats moindres +voire nuls. C'est assez frustrant. C'est certainement dû à l'inexpérience. +Même si la date de rendu sera passée, je ne m'avoue pas vaincu et je continuerai à +bosser sur le DQN sur les états. \ No newline at end of file diff --git a/Sergent/run.py b/Sergent/run.py new file mode 100644 index 0000000..787516d --- /dev/null +++ b/Sergent/run.py @@ -0,0 +1,30 @@ +# You're not allowed to change this file +from ple.games.flappybird import FlappyBird +from ple import PLE +import numpy as np +from FlappyAgent import FlappyPolicy + +game = FlappyBird() +p = PLE(game, fps=30, frame_skip=1, num_steps=1, force_fps=False, display_screen=True) +# Note: if you want to see you agent act in real time, set force_fps to False. But don't use this setting for learning, just for display purposes. + +p.init() +reward = 0.0 + +nb_games = 100 +cumulated = np.zeros((nb_games)) + +for i in range(nb_games): + print('game',i+1,'out of',nb_games) + p.reset_game() + + while(not p.game_over()): + state = game.getGameState() + screen = p.getScreenRGB() + action=FlappyPolicy(state, screen) ### Your job is to define this function. + + reward = p.act(action) + cumulated[i] = cumulated[i] + reward + +average_score = np.mean(cumulated) +max_score = np.max(cumulated) diff --git a/Sergent/screeny_birb.dqf b/Sergent/screeny_birb.dqf new file mode 100644 index 0000000000000000000000000000000000000000..31630dbc06b89787c94f646ddb30c90126592422 GIT binary patch literal 6479416 zcmeFZ2{@MD)-a9`86pXlNR&dEhv(kgkV1(@rOA*Xb7d-NFqKRtWGF+VC{mg|_gYV+ zQ52fziBL33rRjSV=e+Olf8O_e({+8<^*?8Kx%ag8y7yjtO?&P8DZy@>(AlRR8>(5Jt{uBfS#02{K6#Ls{`thp{`qQA; zKXAdk>C*-HkOTM!^8R50Uqt)i2O9pJ|4UK8Zo1W+z6Cq?uUfZ1zueDf{Z)a$Ri0h} 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