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Dies ist eine alte Version des Dokuments!
Ich habe mich, um das Thema besser zu verstehen mit den Classic control Acrobot beschäftigt. (https://gym.openai.com/envs/#classic_control)
Hierbei handelt es sich um ein Doppelpendel, welches als Ziel versucht sich über die Linie (höhe 1) zu schwingen.
Die Schwierigkeit liegt bei diesem Environment darin, dass man einen Weg finden muss, dass NN mit den wenigen Erfolgen zu trainieren. (Anfangs kam das Pendel bei 1000 Versuchen ca. 2 mal über die Linie)
Dazu muss man wissen, dass die klassische KI meist nur durch Belohnungen etwas lernt (die sich durch zwischen Etappen erringen lassen).
Zum Vergleich ich habe das Problem mit zwei unterschiedlichen NN getestet (hier Graphen zum Vergleich)
Der Code zum besten Ergebnis ich lass die Trainingsdaten weg, da ich finde dass es am meisten Spaß macht beim lernen zu zugucken.
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
import keras
#
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
model = Sequential()
model.add(Dense(16, input_dim=self.state_size, activation='relu',
kernel_regularizer=keras.regularizers.l2(0.00001)))
#model.add(Dropout(0.3))
#model.add(Dense(24, activation='relu', kernel_regularizer=keras.regularizers.l2(0.00001)))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss='mse',
optimizer=Adam(lr=self.learning_rate))
return 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]))
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 = 7
env = gym.make('Acrobot-v1')
state_size = env.observation_space.shape[0]
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])#*0.2##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_1000versuche“)
#
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