From 60d90bba0cc525d131d60d707e6ffdfcc98d253c Mon Sep 17 00:00:00 2001 From: Floriane Leblond Date: Sun, 11 Mar 2018 22:10:21 +0100 Subject: [PATCH] Final version --- FlappyPolicy.py | 30 +++++++++++ FlappyTrain.py | 133 ++++++++++++++++++++++++++++++++++++++++++++++++ Q.npy | Bin 0 -> 1451202 bytes 3 files changed, 163 insertions(+) create mode 100644 FlappyPolicy.py create mode 100644 FlappyTrain.py create mode 100644 Q.npy diff --git a/FlappyPolicy.py b/FlappyPolicy.py new file mode 100644 index 0000000..0d4aa84 --- /dev/null +++ b/FlappyPolicy.py @@ -0,0 +1,30 @@ +import numpy as np +from ple.games.flappybird import FlappyBird +from ple import PLE +actions = [None, 119] + + + +def getstate(state): + + a = list(state.values()) + v_pos = np.int(np.round((a[0]-a[3]+512)/10)) # Différence de position verticale entre l'oiseau et le bas du prochain tuyau + #+512 (hauteur totale en pixels) pour ne jamais être négatif + h_pos = np.int(np.round((a[2]+20)/10)) #Distance de l'oiseau au prochain tuyau + + speed = np.int(np.round(a[1])) + S = 'v'+np.str(v_pos)+'h'+np.str(h_pos)+'s'+np.str(speed) #Transformation dans le formalisme string utilisé précédemment + return S + +#Facile de déterminer FlappyPolicy : il suffit d'aller chercher l'info dans la matrice Q +def FlappyPolicy(state, screen): + Q = np.load('Q.npy').item() + S = getstate(state) + if S not in Q: + action = None + elif Q[S][119]>Q[S][None]: + action = 119 + else: + action = None + + return action diff --git a/FlappyTrain.py b/FlappyTrain.py new file mode 100644 index 0000000..0f7bf9c --- /dev/null +++ b/FlappyTrain.py @@ -0,0 +1,133 @@ +#A soul in tension, that's learning to fly... +#Condition-grounded but determined to try + +from ple.games.flappybird import FlappyBird +from ple import PLE +import numpy as np +import random +from IPython.display import clear_output +from datetime import datetime +import os +import glob +import shutil +import time +import matplotlib.pyplot as plt + +start_time = time.time() + +############ +#Constantes# +############ +epochs = 7200 #Nombre de parties à jouer, obtenu par essai-erreur pour avoir une moyenne de 15 pour 100 parties jouées +gamma = 0.8 # discount factor +alpha = 0.6 +prob = 0.9 +epsilon = 0 #Par essai-erreur +print_delay = 100 #Utilisé pour l'affichage + + +########### +#Fonctions# +########### + + +#L'utilisation du vecteur de sortie total du jeu en entrée d'un réseau de neurones ayant été un échec, je me suis tournée vers la version "easy". +#Je considère que la version précédente n'a pas fonctionné parce que le vecteur d'entrée de mon réseau de neurones était trop grand +#Pour pallier ce problème je simplifie ce vecteur d'entrée en ne gardant que les paramètres qui me semblent utiles +#(modifiés pour obtenir des grandeurs "physiques" comme la distance horizontale au prochain tuyau) +#De cette façon on peut conserver les états plutôt que de passer par un réseau de neurones. +#Intuitivement on a besoin de 2 informations : la distance de Flappy bird au prochain tuyau et sa vitesse verticale. +#Ces informations sont stockées sous la forme d'une chaîne de caractères. +def getstate(jeu): + + a = list(jeu.getGameState().values()) + + #Calcul de la distance verticale entre Flappy bird et le prochain tuyau : + #On a "normalisé" pour avoir des valeurs positives et un nombre d'états raisonnables (par essai-erreur) + v_pos = np.int(np.round((a[0]-a[3]+512)/10)) + + #Même chose avec la distance horizontale + h_pos = np.int(np.round((a[2]+20)/10)) + + #Vitesse verticale de l'oiseau + speed = np.int(np.round(a[1])) + + #On stocke ces valeurs dans Q à l'aide d'une chaîne de caractères + S = 'v'+np.str(v_pos)+'h'+np.str(h_pos)+'s'+np.str(speed) + + return S + +Q = {} + +#Les récompenses sont directement les sorties de p.act(action): +reward_dict = { "positive": 1, "negative": 0.0, "tick": 0, "loss": -1000.0, "win": 0.0} + +# Variables de suivi +scores = [] +average = [] +games = [] + +#Initialisation du jeu +jeu = FlappyBird() +p = PLE(jeu, fps=30, frame_skip=1, num_steps=1, force_fps=True, display_screen=True, reward_values = reward_dict) + + +################# +##Apprentissage## +################# + +for i in range(epochs): + p.reset_game() + S = getstate(jeu) + while(not jeu.game_over()): + if S not in Q: + Q[S] = {None : 0, 119 : 0} + if (np.random.random() < epsilon): # Phase d'exploration + if np.random.random() < prob: + action = None + else: + action = 119 + else: # Phase d'exploitation + if Q[S][119] > Q[S][None]: + action = 119 + else: + action = None + r = p.act(action) + + if r == 0: #Flappy bird s'est maintenu en l'air, ce qui est déjà un bon point + reward = 1 + elif r == 1: #Flappy bird a franchi un tuyau ce qui est encore mieux + reward = 100 + else: #Flappy bird est tombé ou a touché un tuyau (ou a touché le plafond) + reward = -1000 + + new_S = getstate(jeu) + if new_S not in Q: + Q[new_S] = {None : 0, 119 : 0} + maxQ = np.maximum(Q[new_S][None],Q[new_S][119]) + Q[S][action] = Q[S][action] + alpha*(reward + gamma*maxQ - Q[S][action]) + S = new_S + + scores.append(jeu.getScore()-reward_dict["loss"]) + + if epsilon > 0.2: + epsilon -= (1.0/epochs) + + ########################## + #Suivi de l'apprentissage# + ########################## + + #Le suivi se fait à une fréquence déterminée par print_delay + #On sauvegarde également la matrice Q à cette fréquence (choix arbitraire) + if ((i+1)%print_delay) == 0: + print("") + print("Game #: %s" % (i+1,)) + print("Score moyen sur les ",print_delay," derniers essais =", np.mean(scores)) + elapsed_time = time.time() - start_time + printime = time.strftime("%H:%M:%S", time.gmtime(elapsed_time)) + print("Temps écoulé : ", printime) + average.append(np.mean(scores)) + games.append(i+1) + np.save('Q',Q) + print('Save done avec moyenne: ' + np.str(np.mean(scores))) + scores = [] \ No newline at end of file diff --git a/Q.npy b/Q.npy new file mode 100644 index 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