Pacman Code
ZIP des Code: pacman_uni.rar
Wenn ihr das Programm selbst ausprobieren möchtet braucht ihr leider alle Pakete von Requirements, selbst damit ist ein Funktionieren alles andere als Garantiert. Wenn ein Windows update kommt geht wahrscheinlich nichts mehr.
import random import gym import numpy as np from collections import deque from keras.models import Sequential from keras.layers import Dense, Dropout from keras.optimizers import Adam from keras.layers import Conv2D, MaxPooling2D, Flatten import keras
input_width = 80 input_channels = 1 conv_n_maps = [32, 64, 64] conv_kernel_sizes = [(8,8), (4,4), (3,3)] conv_strides = [4, 2, 1] conv_paddings = [„SAME“] * 3 #conv_activation = [tf.nn.relu] * 3 n_hidden_in = 64 * 11 * 10 # conv3 has 64 maps of 11×10 each n_hidden = 512 #hidden_activation = tf.nn.relu #n_outputs = env.action_space.n # 9 discrete actions are available #initializer = tf.variance_scaling_initializer()
class DQNAgent:
def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.memory = deque(maxlen=2000) self.gamma = 1.0 # discount rate self.epsilon = 1.0 # exploration rate self.epsilon_min = 0.01 self.epsilon_decay = 0.999 self.learning_rate = 0.001 self.model = self._build_model()
def _build_model(self): # Einfaches NN vision_model = Sequential() vision_model.add(Conv2D(32, (5, 5) ,activation=None, padding='valid', input_shape=state_size)) ## Achtung hier muss die richtige Dimension rein vision_model.add(keras.layers.advanced_activations.LeakyReLU(alpha=0.05)) #1 vision_model.add(MaxPooling2D((2, 2))) #2 vision_model.add(Conv2D(64, (3, 3), activation=None, padding='valid')) #3 vision_model.add(keras.layers.advanced_activations.LeakyReLU(alpha=0.05)) #4 vision_model.add(MaxPooling2D((2, 2))) #5 vision_model.add(Flatten()) #6 #vision_model.add(keras.layers.core.Dropout(dropout, noise_shape=None, seed=None)) #7 vision_model.add(Dense(20,activation=None))#kernel_regularizer=keras.regularizers.l1(reg))) #8 vision_model.add(keras.layers.advanced_activations.LeakyReLU(alpha=0.05)) #9 vision_model.add(Dense(self.action_size,activation='softmax', name='main_output')) #10 vision_model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate)) return vision_model
def remember(self, state, action, reward, next_state, done, total, importance): # merkt sich alle bisher durchlaufenen Zustände self.memory.append([state, action, reward, next_state, done,total,importance])
def act(self, state): # epsilon-greedy: off-policy oder policy if np.random.rand() <= self.epsilon: return random.randrange(self.action_size) act_values = self.model.predict(state) return np.argmax(act_values[0]) # returns action
def replay(self, batch_size): # baut den Vektor der Q-Werte aus # als reward zum Zeitpunkt t + gamma*max(moegliche rewards zum Zeitpunkt t+1) probabilities = np.array([m[-1] for m in self.memory]) probabilities = 1./np.sum(probabilities) * probabilities #print( probabilities.shape) minibatch = [self.memory[i] for i in np.random.choice(range(len(self.memory)),size=batch_size, p=probabilities)] states, targets_f = [], [] for state, action, reward, next_state, done,total,importance in minibatch: target = reward if not done: target = (reward + self.gamma * np.amax(self.model.predict(next_state)[0])) #print("Reward: ", reward) target_f = self.model.predict(state) target_f[0][action] = target # Filtering out states and targets for training states.append(state[0]) targets_f.append(target_f[0]) history = self.model.fit(np.array(states), np.array(targets_f), epochs=1, verbose=0) # Keeping track of loss loss = history.history['loss'][0] if self.epsilon > self.epsilon_min: self.epsilon *= self.epsilon_decay return loss
def load(self, name): self.model.load_weights(name)
def save(self, name): self.model.save_weights(name)
EPISODES = 22
env = gym.make('MsPacman-v0') state_size = env.observation_space.shape action_size = env.action_space.n agent = DQNAgent(state_size, action_size) done = False batch_size = 32
for e in range(EPISODES):
state = env.reset() state = np.reshape(state, (1,)+ state_size) cum_reward = 0 for time in range(500): env.render() action = agent.act(state) next_state, reward, done, _ = env.step(action) #additional_reward = -(state[0,0] + state[0,0]*state[0,2]-state[0,1]*state[0,3])##faktore aus probieren reward = reward #+ additional_reward if not done else 10 # cum_reward += reward next_state = np.reshape(next_state, (1,)+ state_size) agent.remember(state, action, reward, next_state, done,reward,1) state = next_state if done: print("episode: {}/{}, score: {}, e: {:.2}" .format(e, EPISODES, time, agent.epsilon)) break if len(agent.memory) > batch_size: loss = agent.replay(batch_size) # Logging training loss and actual reward every 10 timesteps if time % 10 == 0: print("episode: {}/{}, time: {}, cumulative reward: {:.4f}, loss: {:.4f}".format(e, EPISODES, time, cum_reward, loss)) for i in range(time): pos = -i-1 agent.memory[-i-2][-2] += reward for j in range(-time,pos): new_total = agent.memory[j][-2] + agent.memory[pos][2] mem = agent.memory[j] agent.memory[j][-1] =new_total
for i in range(time): pos = -i-1 imp = max(agent.memory[pos][-2]-agent.model.predict(agent.memory[pos][0])[0,agent.memory[pos][1]],0) mem = agent.memory[pos] agent.memory[pos][-1] = imp agent.save("qlearning_Acrobot_3versuche")
import gym env = gym.make('MsPacman-v0') state_size = env.observation_space.shape action_size = env.action_space.n agent = DQNAgent(state_size, action_size) done = False batch_size = 32 zähler=0
#agent.load(„qlearning_Acrobot_3versuche“)
import time as ti for e in range(100):
state = env.reset() #state[0] = state[0] + np.random.randn()*0.1 #state[1] = state[1] + np.random.randn()*0.1 #state[2] = state[2] + np.random.randn()*0.1 #state[3] = state[3] + np.random.randn()*0.1 #env.env.state = state state = np.reshape(state, [1, state_size]) for time in range(2000): env.render() agent.epsilon = 0 action = agent.act(state) next_state, reward, done, _ = env.step(action) next_state = np.reshape(next_state, [1, state_size]) state = next_state if done: zähler+=1 print (zähler, "Duration: ", time) break else: print ("Volle Zeit")