def main(log_file, h_sizes, improve_loss_min=0.001):
x_train, y_train, x_test, y_test = generate_cases(log_file)
in_size = LINE_MAX_CHAR
out_size = 2
layers = [in_size] + h_sizes + [out_size]
model = FunctionSet()
for li in range(1, len(layers)):
setattr(model, "l%d" % li, F.Linear(layers[li-1], layers[li]))
optimizer = optimizers.SGD()
optimizer.setup(model.collect_parameters())
last_loss = None
for epoch in range(3000000):
optimizer.zero_grads()
loss, accuracy = forward(model, x_train, y_train)
loss.backward()
if epoch % 100 == 0:
print "epoch: %s, loss: %s, accuracy: %s" % (epoch, loss.data, accuracy.data)
if last_loss is not None and last_loss - improve_loss_min < loss.data:
print "Finish Training"
break
last_loss = loss.data
optimizer.update()
if epoch % 1000 == 0:
loss, accuracy = forward(model, x_test, y_test)
print "epoch: %s, Try Test Result: loss: %s, accuracy: %s" % (epoch, loss.data, accuracy.data)
# result
loss, accuracy = forward(model, x_test, y_test)
print "epoch: %s, Test Result: loss: %s, accuracy: %s" % (epoch, loss.data, accuracy.data)
return epoch, accuracy.data
def main(args):
def forward(x_data, y_data):
x = Variable(x_data)
t = Variable(y_data)
h1 = F.relu(model.l1(x)) # activation function
h2 = F.relu(model.l2(h1)) # ReLU does not have parameters to optimize
y = model.l3(h2)
# the loss function of softmax regression
return F.softmax_cross_entropy(y, t), F.accuracy(y, t) # current accuracy
def evaluate():
sum_loss, sum_accuracy = 0, 0
for i in xrange(0, 10000, batchsize):
x_batch = x_test[i:i+batchsize]
y_batch = y_test[i:i+batchsize]
loss, accuracy = forward(x_batch, y_batch)
sum_loss += loss.data * batchsize
sum_accuracy += accuracy.data * batchsize
mean_loss = sum_loss / 10000
mean_accuracy = sum_accuracy / 10000
print(mean_loss[0], mean_accuracy)
return
global debug, verbose
debug = args.debug
if debug == True:
verbose = True
else:
verbose = args.verbose
mnist = fetch_mldata('MNIST original')
x_all = mnist.data.astype(np.float32) / 255 # Scaling features to [0, 1]
y_all = mnist.target.astype(np.int32)
x_train, x_test = np.split(x_all, [60000]) # 60000 for training, 10000 for test
y_train, y_test = np.split(y_all, [60000])
# Simple three layer rectfier network
model = FunctionSet(
l1 = F.Linear(784, 100), # 784 pixels -> 100 units
l2 = F.Linear(100, 100), # 100 units -> 100 units
l3 = F.Linear(100, 10), # 100 units -> 10 digits
)
optimizer = optimizers.SGD()
optimizer.setup(model.collect_parameters())
batchsize = 100
for epoch in xrange(20):
if verbose: logger.info('epoch: {}'.format(epoch))
indexes = np.random.permutation(60000)
for i in xrange(0, 60000, batchsize):
x_batch = x_train[indexes[i:i+batchsize]]
y_batch = y_train[indexes[i:i+batchsize]]
optimizer.zero_grads() # Initialize gradient arrays
loss, accuracy = forward(x_batch, y_batch) # loss function
loss.backward() # Backpropagation
optimizer.update()
evaluate()
return 0
class LinearModel(object):
UNIT_NUM = 10
BATCH_SIZE = 32
EPOCH = 100
def __init__(self, optimizer):
self.model = FunctionSet(
l = Linear(self.UNIT_NUM, 2)
)
self.optimizer = optimizer
# true parameters
self.w = np.random.uniform(-1, 1, (self.UNIT_NUM, 1)).astype(np.float32)
self.b = np.random.uniform(-1, 1, (1, )).astype(np.float32)
def _train_linear_classifier(self, model, optimizer, gpu):
def _make_label(x):
a = (np.dot(x, self.w) + self.b).reshape((self.BATCH_SIZE, ))
t = np.empty_like(a).astype(np.int32)
t[a>=0] = 0
t[a< 0] = 1
return t
def _make_dataset(batch_size, unit_num, gpu):
x_data = np.random.uniform(-1, 1, (batch_size, unit_num)).astype(np.float32)
t_data = _make_label(x_data)
if gpu:
x_data = cuda.to_gpu(x_data)
t_data = cuda.to_gpu(t_data)
x = Variable(x_data)
t = Variable(t_data)
return x, t
for epoch in xrange(self.EPOCH):
x, t = _make_dataset(self.BATCH_SIZE, self.UNIT_NUM, gpu)
optimizer.zero_grads()
y = model.l(x)
loss = softmax_cross_entropy(y, t)
loss.backward()
optimizer.update()
x_test, t_test = _make_dataset(self.BATCH_SIZE, self.UNIT_NUM, gpu)
y_test = model.l(x_test)
return accuracy(y_test, t_test)
def _accuracy_cpu(self):
self.optimizer.setup(self.model.collect_parameters())
return self._train_linear_classifier(self.model, self.optimizer, False)
def _accuracy_gpu(self):
model = self.model
optimizer = self.optimizer
model.to_gpu()
optimizer.setup(model.collect_parameters())
return self._train_linear_classifier(model, optimizer, True)
def accuracy(self, gpu):
if gpu:
return cuda.to_cpu(self._accuracy_gpu().data)
else:
return self._accuracy_cpu().data
def main():
if P.use_mean_var:
conv6_output = 126
else:
conv6_output = 128
if P.model_name is None:
model = FunctionSet(
conv1 = F.Convolution2D( 1, 128, 3, stride=1),
conv2 = F.Convolution2D(128, 128, 3, stride=1),
conv3 = F.Convolution2D(128, 128, 3, stride=1),
conv4 = F.Convolution2D(128, 128, 3, stride=1),
conv5 = F.Convolution2D(128, 128, 3, stride=1),
conv6 = F.Convolution2D(128, conv6_output, 3, stride=1),
conv7 = F.Convolution2D(128, 128, 1, stride=1),
conv8 = F.Convolution2D(128, 1, 1, stride=1)
)
if P.gpu >= 0:
cuda.init(P.gpu)
model.to_gpu()
else:
if P.gpu >= 0:
cuda.init(P.gpu)
model = pickle.load(open(os.path.join(P.model_dir, P.model_name), 'rb'))
optimizer = optimizers.MomentumSGD(lr=P.lr, momentum=P.momentum)
optimizer.setup(model.collect_parameters())
train(model, optimizer)
return
def setup_model(n_dimention, n_units):
model = FunctionSet(l1=F.Linear(n_dimention, n_units),
l2=F.Linear(n_units, n_dimention))
# Setup optimizer
optimizer = optimizers.Adam()
optimizer.setup(model.collect_parameters())
return model, optimizer
class ConvolutionalDenoisingAutoencoder():
def __init__(self, imgsize, n_in_channels, n_out_channels, ksize, stride=1, pad=0, use_cuda=False):
self.model = FunctionSet(
encode=F.Convolution2D(n_in_channels, n_out_channels, ksize, stride, pad),
decode=F.Linear(n_out_channels*(math.floor((imgsize+2*pad-ksize)/stride)+1)**2, n_in_channels*imgsize**2)
)
self.use_cuda = use_cuda
if self.use_cuda:
self.model.to_gpu()
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.model.collect_parameters())
def encode(self, x_var):
return F.sigmoid(self.model.encode(x_var))
def decode(self, x_var):
return self.model.decode(x_var)
def predict(self, x_data):
if self.use_cuda:
x_data = cuda.to_gpu(x_data)
x = Variable(x_data)
p = self.encode(x)
if self.use_cuda:
return cuda.to_cpu(p.data)
else:
return p.data
def cost(self, x_data):
x = Variable(x_data)
t = Variable(x_data.reshape(x_data.shape[0], x_data.shape[1]*x_data.shape[2]*x_data.shape[3]))
h = F.dropout(x)
h = self.encode(h)
y = self.decode(h)
return F.mean_squared_error(y, t)
def train(self, x_data):
if self.use_cuda:
x_data = cuda.to_gpu(x_data)
self.optimizer.zero_grads()
loss = self.cost(x_data)
loss.backward()
self.optimizer.update()
if self.use_cuda:
return float(cuda.to_cpu(loss.data))
else:
return loss.data
def test(self, x_data):
if self.use_cuda:
x_data = cuda.to_gpu(x_data)
loss = self.cost(x_data)
return float(cuda.to_cpu(loss.data))
class NNQLearningPlayer(object):
ALPHA = 0.1
GAMMA = 0.99
E_GREEDY = 0.3
def __init__(self):
self.actions = [1, 2, 3, 4]
self.model = FunctionSet(
l1=F.EmbedID(10, 10),
l2=F.Linear(10, 10),
l3=F.Linear(10, 4),
)
self.optimizer = optimizers.SGD()
self.optimizer.setup(self.model.collect_parameters())
self.last_action = None
self.last_q_list = None
self.training = True
def action(self, state, last_reward):
if self.last_action is not None and self.training:
self.update_q_table(self.last_action, state, last_reward)
next_action = self.select_action(state)
self.last_action = next_action
return self.actions[next_action]
def forward(self, state):
x = Variable(np.array([state], dtype=np.int32))
y = None
for i in range(1, 1000): # 1000 は適当な数
if hasattr(self.model, "l%d" % i):
x = getattr(self.model, "l%d" % i)(x)
else:
y = x
break
return y
def select_action(self, state):
self.last_q_list = self.forward(state)
if self.training and random() < self.E_GREEDY: # http://www.sist.ac.jp/~kanakubo/research/reinforcement_learning.html
return randint(0, len(self.actions)-1)
else:
return np.argmax(self.last_q_list.data)
def update_q_table(self, last_action, cur_state, last_reward):
target_val = last_reward + self.GAMMA * np.max(self.forward(cur_state).data)
self.optimizer.zero_grads()
# 結構無理やりLossを計算・・・ この辺の実装は自信がない
tt = np.copy(self.last_q_list.data)
tt[0][last_action] = target_val
target = Variable(tt)
loss = 0.5 * (target - self.last_q_list) ** 2
loss.grad = np.array([[self.ALPHA]], dtype=np.float32)
loss.backward()
self.optimizer.update()
class DenoisingAutoencoder:
def __init__(
self,
n_input,
n_hidden,
tied=True,
noise=None,
ratio=None,
optimizer=optimizers.Adam(),
loss_function=F.sigmoid_cross_entropy,
activation_function=F.sigmoid,
):
self.model = FunctionSet(encoder=F.Linear(n_input, n_hidden), decoder=F.Linear(n_hidden, n_input))
if tied:
self.model.decoder.W = self.model.encoder.W.T
self.noise = noise
self.ratio = ratio
self.optimizer = optimizer
self.optimizer.setup(self.model.collect_parameters())
self.loss_function = loss_function
self.activation_function = activation_function
def train(self, x_data):
self.optimizer.zero_grads()
loss = self.autoencode(x_data, train=True)
loss.backward()
self.optimizer.update()
return loss
def test(self, x_data):
return self.autoencode(x_data, train=False)
def autoencode(self, x_data, train=True):
x = Variable(x_data)
if self.noise and train:
nx = Variable(self.noise.noise(x_data))
else:
nx = Variable(x_data)
if self.ratio:
h = F.dropout(self.encode(nx), ratio=self.ratio, train=train)
else:
h = self.encode(nx)
y = self.decode(h)
return self.loss_function(y, x)
def encode(self, x):
return self.activation_function(self.model.encoder(x))
def decode(self, x):
return self.activation_function(self.model.decoder(x))
def setup_model(gpu_id, n_channel, n_output):
model = FunctionSet(
conv1=F.Convolution2D(n_channel, 32, 5, pad=2),
conv2=F.Convolution2D(32, 32, 5, pad=2),
conv3=F.Convolution2D(32, 64, 5, pad=2),
fl5=F.Linear(960, 64),
fl6=F.Linear(64, n_output),
)
# optimizer = optimizers.MomentumSGD(lr=1e-03)
optimizer = optimizers.AdaGrad()
optimizer.setup(model.collect_parameters())
mlp = ChainerModel(model, optimizer, forward_function=forward)
return mlp
class DeepLearning:
def __init__(self, input_size, hidden_size, output_size):
self.model = FunctionSet(l1=F.Linear(input_size, hidden_size),
l2=F.Linear(hidden_size, hidden_size),
l3=F.Linear(hidden_size, output_size))
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.model.collect_parameters())
def batch(self, X_train, y_train, batch_size, perm):
train_size = X_train.shape[0]
for i in xrange(0, train_size, batch_size):
X_batch = X_train[perm[i: i+batch_size]]
y_batch = y_train[perm[i: i+batch_size]]
# Chainer用に型変換
x = Variable(X_batch)
t = Variable(y_batch)
self.optimizer.zero_grads()
y = self.forward(x) # 予測結果
loss = F.softmax_cross_entropy(y, t)
loss.backward()
self.optimizer.update()
def forward(self, x, train=True):
h1 = F.dropout(F.sigmoid(self.model.l1(x)), train=train)
h2 = F.dropout(F.sigmoid(self.model.l2(h1)), train=train)
return self.model.l3(h2)
def predicate(self, x_data):
x = np.array([x_data], dtype=np.float32)
x = Variable(x)
y = self.forward(x, train=False)
return np.argmax(y.data)
def save(self, fpath):
pickle.dump(self.model, open(fpath, 'wb'), -1)
def load(self, fpath):
self.model = pickle.load(open(fpath,'rb'))
def main(n_bit, h_sizes):
in_size = n_bit+n_bit+1
out_size = 2**(n_bit+1)
layers = [in_size] + h_sizes + [out_size]
model = FunctionSet()
for li in range(1, len(layers)):
setattr(model, "l%d" % li, F.Linear(layers[li-1], layers[li]))
optimizer = optimizers.SGD()
optimizer.setup(model.collect_parameters())
x_data, t_data = generate_training_cases(n_bit)
for epoch in range(3000000):
optimizer.zero_grads()
loss, accuracy = forward(model, x_data, t_data)
loss.backward()
if epoch % 100 == 0:
print "epoch: %s, loss: %s, accuracy: %s" % (epoch, loss.data, accuracy.data)
if accuracy.data == 1:
break
optimizer.update()
print "epoch: %s, loss: %s, accuracy: %s" % (epoch, loss.data, accuracy.data)
return epoch, accuracy.data
class TestNestedFunctionSet(TestCase):
def setUp(self):
self.fs1 = FunctionSet(
a = MockFunction((1, 2)))
self.fs2 = FunctionSet(
fs1 = self.fs1,
b = MockFunction((3, 4)))
def test_get_sorted_funcs(self):
self.assertItemsEqual([k for (k, v) in self.fs2._get_sorted_funcs()], ('b', 'fs1'))
def test_collect_parameters(self):
p_b = np.zeros((3, 4)).astype(np.float32)
p_a = np.zeros((1, 2)).astype(np.float32)
gp_b = np.ones((3, 4)).astype(np.float32)
gp_a = np.ones((1, 2)).astype(np.float32)
actual = self.fs2.collect_parameters()
self.assertTrue(map(len, actual) == [2, 2])
self.assertTrue((actual[0][0] == p_b).all())
self.assertTrue((actual[0][1] == p_a).all())
self.assertTrue((actual[1][0] == gp_b).all())
self.assertTrue((actual[1][1] == gp_a).all())
def test_pickle_cpu(self):
fs2_serialized = pickle.dumps(self.fs2)
fs2_loaded = pickle.loads(fs2_serialized)
self.assertTrue((self.fs2.b.p == fs2_loaded.b.p).all())
self.assertTrue((self.fs2.fs1.a.p == fs2_loaded.fs1.a.p).all())
@attr.gpu
def test_pickle_gpu(self):
self.fs2.to_gpu()
fs2_serialized = pickle.dumps(self.fs2)
fs2_loaded = pickle.loads(fs2_serialized)
fs2_loaded.to_cpu()
self.fs2.to_cpu()
self.assertTrue((self.fs2.b.p == fs2_loaded.b.p).all())
self.assertTrue((self.fs2.fs1.a.p == fs2_loaded.fs1.a.p).all())
class MNISTNet():
def __init__(self):
n_in = 28 * 28
n_hidden = 100
self.model = FunctionSet(
encode=F.Linear(n_in, n_hidden),
decode=F.Linear(n_hidden, n_in)
)
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.model.collect_parameters())
def encode(self, x_var):
return F.sigmoid(self.model.encode(x_var))
def decode(self, x_var):
return F.sigmoid(self.model.decode(x_var))
def predict(self, x_data):
x = Variable(x_data)
p = self.encode(x)
return p.data
def cost(self, x_data, dropout=True):
x = Variable(x_data)
t = Variable(x_data)
if dropout:
x_n = F.dropout(x, ratio=0.4)
else:
x_n = x
h = self.encode(x_n)
y = self.decode(h)
return F.mean_squared_error(y, t)
def train(self, x_data):
self.optimizer.zero_grads()
loss = self.cost(x_data)
loss.backward()
self.optimizer.update()
return float(loss.data)
def main(n_bit, h1_size):
if h1_size > 0:
model = FunctionSet(
l1=F.Linear(n_bit, h1_size),
l2=F.Linear(h1_size, 2**n_bit)
)
else:
model = FunctionSet(
l1=F.Linear(n_bit, 2**n_bit)
)
optimizer = optimizers.SGD()
optimizer.setup(model.collect_parameters())
x_data, t_data = generate_training_cases(n_bit)
for epoch in range(100000):
optimizer.zero_grads()
loss, accuracy = forward(model, x_data, t_data)
loss.backward()
if epoch % 100 == 0:
print "epoch: %s, loss: %s, accuracy: %s" % (epoch, loss.data, accuracy.data)
if accuracy.data == 1:
break
optimizer.update()
print "epoch: %s, loss: %s, accuracy: %s" % (epoch, loss.data, accuracy.data)
return epoch, accuracy.data
class DQN_class: gamma = 0.99 #initial_exploration = 10**2 initial_exploration = 10**2 replay_size = 32 # Replay (batch) size target_model_update_freq = 10**4 # Target update frequancy. original: 10^4 #data_size = 10**6 data_size = 10**6 def __init__(self, enable_controller=[0, 1, 2, 3, 4, 5, 6, 7, 8]): # """ [ 0, 0], # [ 0, 1], # [ 0,-1], # [ 1, 0], # [ 1, 1], # [ 1,-1], # [-1, 0], # [-1, 1], # [-1,-1]]):""" self.num_of_actions = len(enable_controller) self.enable_controller = enable_controller print "Initializing DQN..." print "CUDA init" #cuda.init() print "Model Building" self.model = FunctionSet( #l1 = F.Linear(INPUT_SIZE, 5000), # input map[100, 100] + v[2] + w[1] + wp[2] l1 = F.Linear(INPUT_SIZE, 100), # input map[100, 100] + v[2] + w[1] + wp[2] #l2 = F.Linear(5000, 1000), #l3 = F.Linear(1000, 500), #l4 = F.Linear(500, 100), #l5 = F.Linear(100, self.num_of_actions, l2 = F.Linear(100, self.num_of_actions, initialW=np.zeros((self.num_of_actions, 100), dtype=np.float32)) ).to_gpu() self.model_target = copy.deepcopy(self.model) print "Initizlizing Optimizer" self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.0001) ### 重要!!!! RMSProp!! self.optimizer.setup(self.model.collect_parameters()) # History Data : D=[s, a, r, s_dash, end_episode_flag] self.D = [np.zeros((self.data_size, INPUT_SIZE), dtype=np.float32), np.zeros(self.data_size, dtype=np.uint8), np.zeros((self.data_size, 1), dtype=np.float32), np.zeros((self.data_size, INPUT_SIZE), dtype=np.float32), np.zeros((self.data_size, 1), dtype=np.bool)] #self.D = [np.zeros((self.data_size, INPUT_SIZE), dtype=np.uint8), # np.zeros(self.data_size, dtype=np.uint8), # np.zeros((self.data_size, 1), dtype=np.int8), # np.zeros((self.data_size, INPUT_SIZE), dtype=np.uint8), # np.zeros((self.data_size, 1), dtype=np.bool)] def forward(self, state, action, Reward, state_dash, episode_end): num_of_batch = state.shape[0] s = Variable(state) s_dash = Variable(state_dash) Q = self.Q_func(s) # Get Q-value # Generate Target Signals tmp = self.Q_func_target(s_dash) # Q(s',*) tmp = list(map(np.max, tmp.data.get())) # max_a Q(s',a) max_Q_dash = np.asanyarray(tmp, dtype=np.float32) target = np.asanyarray(Q.data.get(), dtype=np.float32) for i in xrange(num_of_batch): if not episode_end[i][0]: tmp_ = np.sign(Reward[i]) + self.gamma * max_Q_dash[i] else: tmp_ = np.sign(Reward[i]) #action_index = self.action_to_index(action[i]) #target[i, action_index] = tmp_ target[i, action[i]] = tmp_ # TD-error clipping td = Variable(cuda.to_gpu(target)) - Q # TD error #print "td-error" #print "np.max(td.data) : ", #print np.max(td.data.get()) # 何のためにあるのか不明 td = td_clipとなっている td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division td_clip = td * (abs(td.data) <= 1) + td/abs(td_tmp) * (abs(td.data) > 1) #print "td_clip.data :", #print td_clip.data zero_val = Variable(cuda.to_gpu(np.zeros((self.replay_size, self.num_of_actions))).astype(np.float32)) #zero_val = Variable(cuda.to_gpu(np.zeros((self.replay_size, self.num_of_actions)))) loss = F.mean_squared_error(td_clip, zero_val) return loss, Q # Dataを保存 def stockExperience(self, time, state, action, reward, state_dash, episode_end_flag): data_index = time % self.data_size if episode_end_flag is True: self.D[0][data_index] = state self.D[1][data_index] = action self.D[2][data_index] = reward else: self.D[0][data_index] = state self.D[1][data_index] = action self.D[2][data_index] = reward self.D[3][data_index] = state_dash self.D[4][data_index] = episode_end_flag # mini batch学習 def experienceReplay(self, time): if self.initial_exploration < time: # Pick up replay_size number of samples from the Data if time < self.data_size: # during the first sweep of the History Data replay_index = np.random.randint(0, time, (self.replay_size, 1)) else: replay_index = np.random.randint(0, self.data_size, (self.replay_size, 1)) #s_replay = np.ndarray(shape=(self.replay_size, 100, 100), dtype=np.float32) s_replay = np.ndarray(shape=(self.replay_size, INPUT_SIZE), dtype=np.float32) a_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.uint8) r_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.float32) s_dash_replay = np.ndarray(shape=(self.replay_size, INPUT_SIZE), dtype=np.float32) episode_end_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.bool) for i in xrange(self.replay_size): s_replay[i] = np.asarray(self.D[0][replay_index[i]], dtype=np.float32) a_replay[i] = self.D[1][replay_index[i]] r_replay[i] = self.D[2][replay_index[i]] s_dash_replay[i] = np.array(self.D[3][replay_index[i]], dtype=np.float32) episode_end_replay[i] = self.D[4][replay_index[i]] if i == 0: print "s", s_replay[0][0], s_replay[0][1]*180/np.pi print "a", a_replay[0] print "s\'", s_dash_replay[0][0], s_dash_replay[0][1]*180/np.pi print "r", r_replay[0] s_replay = cuda.to_gpu(s_replay) s_dash_replay = cuda.to_gpu(s_dash_replay) # Gradient-based update self.optimizer.zero_grads() loss, _ = self.forward(s_replay, a_replay, r_replay, s_dash_replay, episode_end_replay) loss.backward() ### 逆伝播 self.optimizer.update() ### 学習!!!!!!!!!, ネットワークの更新 def Q_func(self, state): #h1 = F.relu(self.model.l1(state / 254.0)) # scale inputs into [0.0 1.0] h1 = F.relu(self.model.l1(state)) # scale inputs into [0.0 1.0] #h2 = F.relu(self.model.l2(h1)) #h3 = F.relu(self.model.l3(h2)) #h4 = F.relu(self.model.l4(h3)) #Q = self.model.l5(h4) Q = self.model.l2(h1) return Q def Q_func_target(self, state): #h1 = F.relu(self.model_target.l1(state / 254.0)) # scale inputs into [0.0 1.0] h1 = F.relu(self.model_target.l1(state)) # scale inputs into [0.0 1.0] #h2 = F.relu(self.model_target.l2(h1)) #h3 = F.relu(self.model_target.l3(h2)) #h4 = F.relu(self.model_target.l4(h3)) #Q = self.model.l5(h4) Q = self.model.l2(h1) return Q def e_greedy(self, state, epsilon): s = Variable(state) Q = self.Q_func(s) Q = Q.data if np.random.rand() < epsilon: #index_action = np.random.randint(0, self.num_of_actions) action = np.random.randint(0, self.num_of_actions) print "RANDOM" else: #index_action = np.argmax(Q.get()) action = np.argmax(Q.get()) print "GREEDY" #return self.index_to_action(index_action), Q return action, Q def action_to_vec(self, action, vec): # """ [ 0, 0], # [ 0, 1], # [ 0,-1], # [ 1, 0], # [ 1, 1], # [ 1,-1], # [-1, 0], # [-1, 1], # [-1,-1]]):""" #vec = Twist() if action == 3 or action == 4 or action == 5: #vec.linear.x += 0.1 vec.linear.x = 0.3 elif action == 6 or action == 7 or action == 8: #vec.linear.x -= 0.1 vec.linear.x = -0.3 else: vec.linear.x = 0.0 if action == 1 or action == 4 or action == 7: #vec.angular.z += 0.1 vec.angular.z = 0.3 elif action == 2 or action == 5 or action == 8: #vec.angular.z -= 0.1 vec.angular.z = -0.3 else: vec.angular.z = 0.0 if vec.linear.x > 1: vec.linear.x = 1 elif vec.linear.x < -1: vec.linear.x = -1 if vec.angular.z > 1: vec.angular.z = 1 elif vec.angular.z < -1: vec.angular.z = -1 return vec
class DQN_class:
# Hyper-Parameters
gamma = 0.99 # Discount factor
initial_exploration = 10**3 # Initial exploratoin. original
replay_size = 32 # Replay (batch) size
target_model_update_freq = 10**2 # Target update frequancy. original
data_size = 10**5 # Data size of history. original
#actions are 0 => do nothing, 1 -> buy, -1 sell
def __init__(self, input_vector_length,enable_controller=[0, 1, 2]):
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller # Default setting : "Pong"
self.input_vector_length = input_vector_length
print "Initializing DQN..."
# Initialization for Chainer 1.1.0 or older.
# print "CUDA init"
# cuda.init()
#inputs --> 5 * 14 (with 10 temporality) + 5 (of last one hour) + 5 (of last 24 hour)
print "Model Building"
self.model = FunctionSet(
l1=F.Linear(input_vector_length, 500),
l2=F.Linear(500, 250),
l3=F.Linear(250, 80),
q_value=F.Linear(80, self.num_of_actions,
initialW=np.zeros((self.num_of_actions, 80),
dtype=np.float32))
).to_gpu()
print "Initizlizing Optimizer"
self.optimizer = optimizers.RMSpropGraves(lr=0.0002, alpha=0.3, momentum=0.2)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.D = [np.zeros((self.data_size, self.input_vector_length), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, self.input_vector_length), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)]
def forward(self, state, action, Reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
Q = self.Q_func(s) # Get Q-value
# Generate Target Signals
max_Q_dash_ = self.Q_func(s_dash)
tmp = list(map(np.max, max_Q_dash_.data.get()))
max_Q_dash = np.asanyarray(tmp, dtype=np.float32)
target = np.asanyarray(Q.data.get(), dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = np.sign(Reward[i]) + self.gamma * max_Q_dash[i]
else:
tmp_ = np.sign(Reward[i])
target[i, self.action_to_index(action[i])] = tmp_
loss = F.mean_squared_error(Variable(cuda.to_gpu(target)), Q)
return loss, Q
def stockExperience(self, time,
state, action, reward, state_dash,
episode_end_flag):
data_index = time % self.data_size
if episode_end_flag is True:
self.D[0][data_index] = state
self.D[1][data_index] = action
self.D[2][data_index] = reward
else:
self.D[0][data_index] = state
self.D[1][data_index] = action
self.D[2][data_index] = reward
self.D[3][data_index] = state_dash
self.D[4][data_index] = episode_end_flag
def experienceReplay(self, time):
if self.initial_exploration < time:
# Pick up replay_size number of samples from the Data
if time < self.data_size: # during the first sweep of the History Data
replay_index = np.random.randint(0, time, (self.replay_size, 1))
else:
replay_index = np.random.randint(0, self.data_size, (self.replay_size, 1))
s_replay = np.ndarray(shape=(self.replay_size, self.input_vector_length), dtype=np.float32)
a_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.uint8)
r_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.float32)
s_dash_replay = np.ndarray(shape=(self.replay_size, self.input_vector_length), dtype=np.float32)
episode_end_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.bool)
for i in xrange(self.replay_size):
s_replay[i] = np.asarray(self.D[0][replay_index[i]], dtype=np.float32)
a_replay[i] = self.D[1][replay_index[i]]
r_replay[i] = self.D[2][replay_index[i]]
s_dash_replay[i] = np.array(self.D[3][replay_index[i]], dtype=np.float32)
episode_end_replay[i] = self.D[4][replay_index[i]]
s_replay = cuda.to_gpu(s_replay)
s_dash_replay = cuda.to_gpu(s_dash_replay)
# Gradient-based update
self.optimizer.zero_grads()
loss, _ = self.forward(s_replay, a_replay, r_replay, s_dash_replay, episode_end_replay)
loss.backward()
self.optimizer.update()
def Q_func(self, state):
#todo might want to normalize input, but for now I will do that outside this class
h1 = F.relu(self.model.l1(state))
h2 = F.relu(self.model.l2(h1))
h3 = F.relu(self.model.l3(h2))
Q = self.model.q_value(h3)
return Q
def e_greedy(self, state, epsilon):
s = Variable(state)
Q = self.Q_func(s)
Q = Q.data
if np.random.rand() < epsilon:
index_action = np.random.randint(0, self.num_of_actions)
print "RANDOM"
else:
index_action = np.argmax(Q.get())
print "GREEDY"
return self.index_to_action(index_action), Q
def target_model_update(self):
self.model_target = copy.deepcopy(self.model)
def index_to_action(self, index_of_action):
return self.enable_controller[index_of_action]
def action_to_index(self, action):
return self.enable_controller.index(action)
class DQN_class:
# Hyper-Parameters
gamma = 0.99 # Discount factor
def __init__(self, enable_controller=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]):
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller # Default setting : "Pong"
print "Initializing DQN..."
# Initialization of Chainer 1.1.0 or older.
# print "CUDA init"
# cuda.init()
print "Model Building"
w = math.sqrt(2) # MSRA scaling
self.model = FunctionSet(
conv1=F.Convolution2D(3, 64, 7, wscale=w, stride=2, pad=3),
conv2_1_1=F.Convolution2D(64, 64, 1, wscale=w, stride=1),
conv2_1_2=F.Convolution2D(64, 64, 3, wscale=w, stride=1, pad=1),
conv2_1_3=F.Convolution2D(64, 256, 1, wscale=w, stride=1),
conv2_1_ex=F.Convolution2D(64, 256, 1, wscale=w, stride=1),
conv2_2_1=F.Convolution2D(256, 64, 1, wscale=w, stride=1),
conv2_2_2=F.Convolution2D(64, 64, 3, wscale=w, stride=1, pad=1),
conv2_2_3=F.Convolution2D(64, 256, 1, wscale=w, stride=1),
conv2_3_1=F.Convolution2D(256, 64, 1, wscale=w, stride=1),
conv2_3_2=F.Convolution2D(64, 64, 3, wscale=w, stride=1, pad=1),
conv2_3_3=F.Convolution2D(64, 256, 1, wscale=w, stride=1),
conv3_1_1=F.Convolution2D(256, 128, 1, wscale=w, stride=2),
conv3_1_2=F.Convolution2D(128, 128, 3, wscale=w, stride=1, pad=1),
conv3_1_3=F.Convolution2D(128, 512, 1, wscale=w, stride=1),
conv3_1_ex=F.Convolution2D(256, 512, 1, wscale=w, stride=2),
conv3_2_1=F.Convolution2D(512, 128, 1, wscale=w, stride=1),
conv3_2_2=F.Convolution2D(128, 128, 3, wscale=w, stride=1, pad=1),
conv3_2_3=F.Convolution2D(128, 512, 1, wscale=w, stride=1),
conv3_3_1=F.Convolution2D(512, 128, 1, wscale=w, stride=1),
conv3_3_2=F.Convolution2D(128, 128, 3, wscale=w, stride=1, pad=1),
conv3_3_3=F.Convolution2D(128, 512, 1, wscale=w, stride=1),
conv3_4_1=F.Convolution2D(512, 128, 1, wscale=w, stride=1),
conv3_4_2=F.Convolution2D(128, 128, 3, wscale=w, stride=1, pad=1),
conv3_4_3=F.Convolution2D(128, 512, 1, wscale=w, stride=1),
conv3_5_1=F.Convolution2D(512, 128, 1, wscale=w, stride=1),
conv3_5_2=F.Convolution2D(128, 128, 3, wscale=w, stride=1, pad=1),
conv3_5_3=F.Convolution2D(128, 512, 1, wscale=w, stride=1),
conv3_6_1=F.Convolution2D(512, 128, 1, wscale=w, stride=1),
conv3_6_2=F.Convolution2D(128, 128, 3, wscale=w, stride=1, pad=1),
conv3_6_3=F.Convolution2D(128, 512, 1, wscale=w, stride=1),
conv3_7_1=F.Convolution2D(512, 128, 1, wscale=w, stride=1),
conv3_7_2=F.Convolution2D(128, 128, 3, wscale=w, stride=1, pad=1),
conv3_7_3=F.Convolution2D(128, 512, 1, wscale=w, stride=1),
conv3_8_1=F.Convolution2D(512, 128, 1, wscale=w, stride=1),
conv3_8_2=F.Convolution2D(128, 128, 3, wscale=w, stride=1, pad=1),
conv3_8_3=F.Convolution2D(128, 512, 1, wscale=w, stride=1),
conv4_1_1=F.Convolution2D(512, 256, 1, wscale=w, stride=2),
conv4_1_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_1_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_1_ex=F.Convolution2D(512, 1024, 1, wscale=w, stride=2),
conv4_2_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_2_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_2_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_3_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_3_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_3_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_4_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_4_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_4_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_5_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_5_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_5_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_6_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_6_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_6_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_7_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_7_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_7_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_8_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_8_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_8_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_9_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_9_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_9_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_10_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_10_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_10_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_11_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_11_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_11_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_12_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_12_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_12_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_13_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_13_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_13_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_14_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_14_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_14_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_15_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_15_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_15_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_16_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_16_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_16_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_17_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_17_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_17_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_18_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_18_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_18_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_19_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_19_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_19_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_20_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_20_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_20_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_21_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_21_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_21_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_22_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_22_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_22_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_23_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_23_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_23_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_24_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_24_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_24_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_25_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_25_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_25_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_26_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_26_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_26_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_27_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_27_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_27_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_28_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_28_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_28_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_29_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_29_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_29_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_30_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_30_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_30_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_31_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_31_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_31_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_32_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_32_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_32_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_33_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_33_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_33_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_34_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_34_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_34_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_35_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_35_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_35_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv4_36_1=F.Convolution2D(1024, 256, 1, wscale=w, stride=1),
conv4_36_2=F.Convolution2D(256, 256, 3, wscale=w, stride=1, pad=1),
conv4_36_3=F.Convolution2D(256, 1024, 1, wscale=w, stride=1),
conv5_1_1=F.Convolution2D(1024, 512, 1, wscale=w, stride=2),
conv5_1_2=F.Convolution2D(512, 512, 3, wscale=w, stride=1, pad=1),
conv5_1_3=F.Convolution2D(512, 2048, 1, wscale=w, stride=1),
conv5_1_ex=F.Convolution2D(1024, 2048, 1, wscale=w, stride=2),
conv5_2_1=F.Convolution2D(2048, 512, 1, wscale=w, stride=1),
conv5_2_2=F.Convolution2D(512, 512, 3, wscale=w, stride=1, pad=1),
conv5_2_3=F.Convolution2D(512, 2048, 1, wscale=w, stride=1),
conv5_3_1=F.Convolution2D(2048, 512, 1, wscale=w, stride=1),
conv5_3_2=F.Convolution2D(512, 512, 3, wscale=w, stride=1, pad=1),
conv5_3_3=F.Convolution2D(512, 2048, 1, wscale=w, stride=1),
q_value=F.Linear(2048, self.num_of_actions,
initialW=np.zeros((self.num_of_actions, 2048),
dtype=np.float32))
)
self.model_target = copy.deepcopy(self.model)
print "Initizlizing Optimizer"
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.model.collect_parameters())
def forward(self, state, action, Reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
Q = self.Q_func(s) # Get Q-value
# Generate Target Signals
tmp = self.Q_func_target(s_dash) # Q(s',*)
tmp = list(map(np.max, tmp.data.get())) # max_a Q(s',a)
max_Q_dash = np.asanyarray(tmp, dtype=np.float32)
target = np.asanyarray(Q.data.get(), dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = np.sign(Reward[i]) + self.gamma * max_Q_dash[i]
else:
tmp_ = np.sign(Reward[i])
action_index = self.action_to_index(action[i])
target[i, action_index] = tmp_
# TD-error clipping
td = Variable(cuda.to_gpu(target)) - Q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td/abs(td_tmp) * (abs(td.data) > 1)
zero_val = Variable(cuda.to_gpu(np.zeros((num_of_batch, self.num_of_actions), dtype=np.float32)))
loss = F.mean_squared_error(td_clip, zero_val)
return loss, Q
def Q_func(self, state):
h = F.relu(self.model.conv1(state))
h = F.max_pooling_2d(h, 3, stride=2)
h_rem = self.model.conv2_1_ex(h)
h = F.relu(self.model.conv2_1_1(h))
h = F.relu(self.model.conv2_1_2(h))
h = self.model.conv2_1_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv2_2_1(h))
h = F.relu(self.model.conv2_2_2(h))
h = self.model.conv2_2_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv2_3_1(h))
h = F.relu(self.model.conv2_3_2(h))
h = self.model.conv2_3_3(h)
h = F.relu(h + h_rem)
h_rem = self.model.conv3_1_ex(h)
h = F.relu(self.model.conv3_1_1(h))
h = F.relu(self.model.conv3_1_2(h))
h = self.model.conv3_1_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv3_2_1(h))
h = F.relu(self.model.conv3_2_2(h))
h = self.model.conv3_2_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv3_3_1(h))
h = F.relu(self.model.conv3_3_2(h))
h = self.model.conv3_3_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv3_4_1(h))
h = F.relu(self.model.conv3_4_2(h))
h = self.model.conv3_4_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv3_5_1(h))
h = F.relu(self.model.conv3_5_2(h))
h = self.model.conv3_5_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv3_6_1(h))
h = F.relu(self.model.conv3_6_2(h))
h = self.model.conv3_6_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv3_7_1(h))
h = F.relu(self.model.conv3_7_2(h))
h = self.model.conv3_7_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv3_8_1(h))
h = F.relu(self.model.conv3_8_2(h))
h = self.model.conv3_8_3(h)
h = F.relu(h + h_rem)
h_rem = self.model.conv4_1_ex(h)
h = F.relu(self.model.conv4_1_1(h))
h = F.relu(self.model.conv4_1_2(h))
h = self.model.conv4_1_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_2_1(h))
h = F.relu(self.model.conv4_2_2(h))
h = self.model.conv4_2_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_3_1(h))
h = F.relu(self.model.conv4_3_2(h))
h = self.model.conv4_3_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_4_1(h))
h = F.relu(self.model.conv4_4_2(h))
h = self.model.conv4_4_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_5_1(h))
h = F.relu(self.model.conv4_5_2(h))
h = self.model.conv4_5_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_6_1(h))
h = F.relu(self.model.conv4_6_2(h))
h = self.model.conv4_6_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_7_1(h))
h = F.relu(self.model.conv4_7_2(h))
h = self.model.conv4_7_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_8_1(h))
h = F.relu(self.model.conv4_8_2(h))
h = self.model.conv4_8_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_9_1(h))
h = F.relu(self.model.conv4_9_2(h))
h = self.model.conv4_9_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_10_1(h))
h = F.relu(self.model.conv4_10_2(h))
h = self.model.conv4_10_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_11_1(h))
h = F.relu(self.model.conv4_11_2(h))
h = self.model.conv4_11_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_12_1(h))
h = F.relu(self.model.conv4_12_2(h))
h = self.model.conv4_12_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_13_1(h))
h = F.relu(self.model.conv4_13_2(h))
h = self.model.conv4_13_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_14_1(h))
h = F.relu(self.model.conv4_14_2(h))
h = self.model.conv4_14_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_15_1(h))
h = F.relu(self.model.conv4_15_2(h))
h = self.model.conv4_15_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_16_1(h))
h = F.relu(self.model.conv4_16_2(h))
h = self.model.conv4_16_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_17_1(h))
h = F.relu(self.model.conv4_17_2(h))
h = self.model.conv4_17_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_18_1(h))
h = F.relu(self.model.conv4_18_2(h))
h = self.model.conv4_18_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_19_1(h))
h = F.relu(self.model.conv4_19_2(h))
h = self.model.conv4_19_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_20_1(h))
h = F.relu(self.model.conv4_20_2(h))
h = self.model.conv4_20_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_21_1(h))
h = F.relu(self.model.conv4_21_2(h))
h = self.model.conv4_21_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_22_1(h))
h = F.relu(self.model.conv4_22_2(h))
h = self.model.conv4_22_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_23_1(h))
h = F.relu(self.model.conv4_23_2(h))
h = self.model.conv4_23_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_24_1(h))
h = F.relu(self.model.conv4_24_2(h))
h = self.model.conv4_24_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_25_1(h))
h = F.relu(self.model.conv4_25_2(h))
h = self.model.conv4_25_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_26_1(h))
h = F.relu(self.model.conv4_26_2(h))
h = self.model.conv4_26_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_27_1(h))
h = F.relu(self.model.conv4_27_2(h))
h = self.model.conv4_27_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_28_1(h))
h = F.relu(self.model.conv4_28_2(h))
h = self.model.conv4_28_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_29_1(h))
h = F.relu(self.model.conv4_29_2(h))
h = self.model.conv4_29_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_30_1(h))
h = F.relu(self.model.conv4_30_2(h))
h = self.model.conv4_30_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_31_1(h))
h = F.relu(self.model.conv4_31_2(h))
h = self.model.conv4_31_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_32_1(h))
h = F.relu(self.model.conv4_32_2(h))
h = self.model.conv4_32_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_33_1(h))
h = F.relu(self.model.conv4_33_2(h))
h = self.model.conv4_33_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_34_1(h))
h = F.relu(self.model.conv4_34_2(h))
h = self.model.conv4_34_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_35_1(h))
h = F.relu(self.model.conv4_35_2(h))
h = self.model.conv4_35_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv4_36_1(h))
h = F.relu(self.model.conv4_36_2(h))
h = self.model.conv4_36_3(h)
h = F.relu(h + h_rem)
h_rem = self.model.conv5_1_ex(h)
h = F.relu(self.model.conv5_1_1(h))
h = F.relu(self.model.conv5_1_2(h))
h = self.model.conv5_1_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv5_2_1(h))
h = F.relu(self.model.conv5_2_2(h))
h = self.model.conv5_2_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model.conv5_3_1(h))
h = F.relu(self.model.conv5_3_2(h))
h = self.model.conv5_3_3(h)
h = F.relu(h + h_rem)
h = F.average_pooling_2d(h, 7)
Q = self.model.q_value(h)
return Q
def Q_func_target(self, state):
h = F.relu(self.model_target.conv1(state))
h = F.max_pooling_2d(h, 3, stride=2)
h_rem = self.model_target.conv2_1_ex(h)
h = F.relu(self.model_target.conv2_1_1(h))
h = F.relu(self.model_target.conv2_1_2(h))
h = self.model_target.conv2_1_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv2_2_1(h))
h = F.relu(self.model_target.conv2_2_2(h))
h = self.model_target.conv2_2_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv2_3_1(h))
h = F.relu(self.model_target.conv2_3_2(h))
h = self.model_target.conv2_3_3(h)
h = F.relu(h + h_rem)
h_rem = self.model_target.conv3_1_ex(h)
h = F.relu(self.model_target.conv3_1_1(h))
h = F.relu(self.model_target.conv3_1_2(h))
h = self.model_target.conv3_1_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv3_2_1(h))
h = F.relu(self.model_target.conv3_2_2(h))
h = self.model_target.conv3_2_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv3_3_1(h))
h = F.relu(self.model_target.conv3_3_2(h))
h = self.model_target.conv3_3_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv3_4_1(h))
h = F.relu(self.model_target.conv3_4_2(h))
h = self.model_target.conv3_4_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv3_5_1(h))
h = F.relu(self.model_target.conv3_5_2(h))
h = self.model_target.conv3_5_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv3_6_1(h))
h = F.relu(self.model_target.conv3_6_2(h))
h = self.model_target.conv3_6_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv3_7_1(h))
h = F.relu(self.model_target.conv3_7_2(h))
h = self.model_target.conv3_7_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv3_8_1(h))
h = F.relu(self.model_target.conv3_8_2(h))
h = self.model_target.conv3_8_3(h)
h = F.relu(h + h_rem)
h_rem = self.model_target.conv4_1_ex(h)
h = F.relu(self.model_target.conv4_1_1(h))
h = F.relu(self.model_target.conv4_1_2(h))
h = self.model_target.conv4_1_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_2_1(h))
h = F.relu(self.model_target.conv4_2_2(h))
h = self.model_target.conv4_2_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_3_1(h))
h = F.relu(self.model_target.conv4_3_2(h))
h = self.model_target.conv4_3_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_4_1(h))
h = F.relu(self.model_target.conv4_4_2(h))
h = self.model_target.conv4_4_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_5_1(h))
h = F.relu(self.model_target.conv4_5_2(h))
h = self.model_target.conv4_5_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_6_1(h))
h = F.relu(self.model_target.conv4_6_2(h))
h = self.model_target.conv4_6_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_7_1(h))
h = F.relu(self.model_target.conv4_7_2(h))
h = self.model_target.conv4_7_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_8_1(h))
h = F.relu(self.model_target.conv4_8_2(h))
h = self.model_target.conv4_8_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_9_1(h))
h = F.relu(self.model_target.conv4_9_2(h))
h = self.model_target.conv4_9_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_10_1(h))
h = F.relu(self.model_target.conv4_10_2(h))
h = self.model_target.conv4_10_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_11_1(h))
h = F.relu(self.model_target.conv4_11_2(h))
h = self.model_target.conv4_11_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_12_1(h))
h = F.relu(self.model_target.conv4_12_2(h))
h = self.model_target.conv4_12_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_13_1(h))
h = F.relu(self.model_target.conv4_13_2(h))
h = self.model_target.conv4_13_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_14_1(h))
h = F.relu(self.model_target.conv4_14_2(h))
h = self.model_target.conv4_14_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_15_1(h))
h = F.relu(self.model_target.conv4_15_2(h))
h = self.model_target.conv4_15_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_16_1(h))
h = F.relu(self.model_target.conv4_16_2(h))
h = self.model_target.conv4_16_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_17_1(h))
h = F.relu(self.model_target.conv4_17_2(h))
h = self.model_target.conv4_17_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_18_1(h))
h = F.relu(self.model_target.conv4_18_2(h))
h = self.model_target.conv4_18_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_19_1(h))
h = F.relu(self.model_target.conv4_19_2(h))
h = self.model_target.conv4_19_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_20_1(h))
h = F.relu(self.model_target.conv4_20_2(h))
h = self.model_target.conv4_20_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_21_1(h))
h = F.relu(self.model_target.conv4_21_2(h))
h = self.model_target.conv4_21_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_22_1(h))
h = F.relu(self.model_target.conv4_22_2(h))
h = self.model_target.conv4_22_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_23_1(h))
h = F.relu(self.model_target.conv4_23_2(h))
h = self.model_target.conv4_23_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_24_1(h))
h = F.relu(self.model_target.conv4_24_2(h))
h = self.model_target.conv4_24_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_25_1(h))
h = F.relu(self.model_target.conv4_25_2(h))
h = self.model_target.conv4_25_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_26_1(h))
h = F.relu(self.model_target.conv4_26_2(h))
h = self.model_target.conv4_26_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_27_1(h))
h = F.relu(self.model_target.conv4_27_2(h))
h = self.model_target.conv4_27_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_28_1(h))
h = F.relu(self.model_target.conv4_28_2(h))
h = self.model_target.conv4_28_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_29_1(h))
h = F.relu(self.model_target.conv4_29_2(h))
h = self.model_target.conv4_29_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_30_1(h))
h = F.relu(self.model_target.conv4_30_2(h))
h = self.model_target.conv4_30_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_31_1(h))
h = F.relu(self.model_target.conv4_31_2(h))
h = self.model_target.conv4_31_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_32_1(h))
h = F.relu(self.model_target.conv4_32_2(h))
h = self.model_target.conv4_32_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_33_1(h))
h = F.relu(self.model_target.conv4_33_2(h))
h = self.model_target.conv4_33_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_34_1(h))
h = F.relu(self.model_target.conv4_34_2(h))
h = self.model_target.conv4_34_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_35_1(h))
h = F.relu(self.model_target.conv4_35_2(h))
h = self.model_target.conv4_35_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv4_36_1(h))
h = F.relu(self.model_target.conv4_36_2(h))
h = self.model_target.conv4_36_3(h)
h = F.relu(h + h_rem)
h_rem = self.model_target.conv5_1_ex(h)
h = F.relu(self.model_target.conv5_1_1(h))
h = F.relu(self.model_target.conv5_1_2(h))
h = self.model_target.conv5_1_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv5_2_1(h))
h = F.relu(self.model_target.conv5_2_2(h))
h = self.model_target.conv5_2_3(h)
h = F.relu(h + h_rem)
h_rem = h
h = F.relu(self.model_target.conv5_3_1(h))
h = F.relu(self.model_target.conv5_3_2(h))
h = self.model_target.conv5_3_3(h)
h = F.relu(h + h_rem)
h = F.average_pooling_2d(h, 7)
Q = self.model_target.q_value(h)
return Q
def e_greedy(self, state, epsilon):
s = Variable(state)
Q = self.Q_func(s)
Q = Q.data
if np.random.rand() < epsilon:
index_action = np.random.randint(0, self.num_of_actions)
print "RANDOM"
else:
index_action = np.argmax(Q.get())
print "GREEDY"
return self.index_to_action(index_action)
def target_model_update(self):
self.model_target = copy.deepcopy(self.model)
def index_to_action(self, index_of_action):
return self.enable_controller[index_of_action]
def action_to_index(self, action):
return self.enable_controller.index(action)
class DQN_class:
# Hyper-Parameters
gamma = 0.99 # Discount factor
initial_exploration = 10**4 # Initial exploratoin. original: 5x10^4
replay_size = 32 # Replay (batch) size
target_model_update_freq = 10**4 # Target update frequancy. original: 10^4
data_size = 10**5 # Data size of history. original: 10^6
def __init__(self, enable_controller=[0, 3, 4]):
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller # Default setting : "Pong"
print "Initializing DQN..."
# Initialization for Chainer 1.1.0 or older.
# print "CUDA init"
# cuda.init()
print "Model Building"
self.model = FunctionSet(
l1=F.Convolution2D(4, 16, ksize=8, stride=4, wscale=np.sqrt(2)),
l2=F.Convolution2D(16, 32, ksize=4, stride=2, wscale=np.sqrt(2)),
l3=F.Linear(2592, 256),
q_value=F.Linear(256, self.num_of_actions,
initialW=np.zeros((self.num_of_actions, 256),
dtype=np.float32))
).to_gpu()
print "Initizlizing Optimizer"
self.optimizer = optimizers.RMSpropGraves(lr=0.0002, alpha=0.3, momentum=0.2)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.D = [np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)]
def forward(self, state, action, Reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
Q = self.Q_func(s) # Get Q-value
# Generate Target Signals
max_Q_dash_ = self.Q_func(s_dash)
tmp = list(map(np.max, max_Q_dash_.data.get()))
max_Q_dash = np.asanyarray(tmp, dtype=np.float32)
target = np.asanyarray(Q.data.get(), dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = np.sign(Reward[i]) + self.gamma * max_Q_dash[i]
else:
tmp_ = np.sign(Reward[i])
target[i, self.action_to_index(action[i])] = tmp_
loss = F.mean_squared_error(Variable(cuda.to_gpu(target)), Q)
return loss, Q
def stockExperience(self, time,
state, action, reward, state_dash,
episode_end_flag):
data_index = time % self.data_size
if episode_end_flag is True:
self.D[0][data_index] = state
self.D[1][data_index] = action
self.D[2][data_index] = reward
else:
self.D[0][data_index] = state
self.D[1][data_index] = action
self.D[2][data_index] = reward
self.D[3][data_index] = state_dash
self.D[4][data_index] = episode_end_flag
def experienceReplay(self, time):
if self.initial_exploration < time:
# Pick up replay_size number of samples from the Data
if time < self.data_size: # during the first sweep of the History Data
replay_index = np.random.randint(0, time, (self.replay_size, 1))
else:
replay_index = np.random.randint(0, self.data_size, (self.replay_size, 1))
s_replay = np.ndarray(shape=(self.replay_size, 4, 84, 84), dtype=np.float32)
a_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.uint8)
r_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.float32)
s_dash_replay = np.ndarray(shape=(self.replay_size, 4, 84, 84), dtype=np.float32)
episode_end_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.bool)
for i in xrange(self.replay_size):
s_replay[i] = np.asarray(self.D[0][replay_index[i]], dtype=np.float32)
a_replay[i] = self.D[1][replay_index[i]]
r_replay[i] = self.D[2][replay_index[i]]
s_dash_replay[i] = np.array(self.D[3][replay_index[i]], dtype=np.float32)
episode_end_replay[i] = self.D[4][replay_index[i]]
s_replay = cuda.to_gpu(s_replay)
s_dash_replay = cuda.to_gpu(s_dash_replay)
# Gradient-based update
self.optimizer.zero_grads()
loss, _ = self.forward(s_replay, a_replay, r_replay, s_dash_replay, episode_end_replay)
loss.backward()
self.optimizer.update()
def Q_func(self, state):
h1 = F.relu(self.model.l1(state / 254.0)) # scale inputs in [0.0, 1.0]
h2 = F.relu(self.model.l2(h1))
h3 = F.relu(self.model.l3(h2))
Q = self.model.q_value(h3)
return Q
def e_greedy(self, state, epsilon):
s = Variable(state)
Q = self.Q_func(s)
Q = Q.data
if np.random.rand() < epsilon:
index_action = np.random.randint(0, self.num_of_actions)
print "RANDOM"
else:
index_action = np.argmax(Q.get())
print "GREEDY"
return self.index_to_action(index_action), Q
def index_to_action(self, index_of_action):
return self.enable_controller[index_of_action]
def action_to_index(self, action):
return self.enable_controller.index(action)
class DQN_class:
# Hyper-Parameters
gamma = 0.99 # Discount factor
initial_exploration = 100#10**4 # Initial exploratoin. original: 5x10^4
replay_size = 32 # Replay (batch) size
target_model_update_freq = 10**4 # Target update frequancy. original: 10^4
data_size = 10**5 #10**5 # Data size of history. original: 10^6
def __init__(self, enable_controller=[0, 3, 4]):
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller # Default setting : "Pong"
print "Initializing DQN..."
print "Model Building"
self.CNN_model = FunctionSet(
l1=F.Convolution2D(4, 32, ksize=8, stride=4, nobias=False, wscale=np.sqrt(2)),
l2=F.Convolution2D(32, 64, ksize=4, stride=2, nobias=False, wscale=np.sqrt(2)),
l3=F.Convolution2D(64, 64, ksize=3, stride=1, nobias=False, wscale=np.sqrt(2)),
)
self.model = FunctionSet(
l4=F.Linear(3136, 512, wscale=np.sqrt(2)),
q_value=F.Linear(512, self.num_of_actions,
initialW=np.zeros((self.num_of_actions, 512),
dtype=np.float32))
).to_gpu()
d = 'elite/'
self.CNN_model.l1.W.data = np.load(d+'l1_W.npy')#.astype(np.float32)
self.CNN_model.l1.b.data = np.load(d+'l1_b.npy')#.astype(np.float32)
self.CNN_model.l2.W.data = np.load(d+'l2_W.npy')#.astype(np.float32)
self.CNN_model.l2.b.data = np.load(d+'l2_b.npy')#.astype(np.float32)
self.CNN_model.l3.W.data = np.load(d+'l3_W.npy')#.astype(np.float32)
self.CNN_model.l3.b.data = np.load(d+'l3_b.npy')#.astype(np.float32)
self.CNN_model = self.CNN_model.to_gpu()
self.CNN_model_target = copy.deepcopy(self.CNN_model)
self.model_target = copy.deepcopy(self.model)
print "Initizlizing Optimizer"
self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.0001)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.D = [np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool),
np.zeros((self.data_size, 1), dtype=np.uint8)]
def forward(self, state, action, Reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
Q = self.Q_func(s) # Get Q-value
# Generate Target Signals
tmp = self.Q_func_target(s_dash) # Q(s',*)
tmp = list(map(np.max, tmp.data.get())) # max_a Q(s',a)
max_Q_dash = np.asanyarray(tmp, dtype=np.float32)
target = np.asanyarray(Q.data.get(), dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = np.sign(Reward[i]) + self.gamma * max_Q_dash[i]
else:
tmp_ = np.sign(Reward[i])
action_index = self.action_to_index(action[i])
target[i, action_index] = tmp_
# TD-error clipping
td = Variable(cuda.to_gpu(target)) - Q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td/abs(td_tmp) * (abs(td.data) > 1)
zero_val = Variable(cuda.to_gpu(np.zeros((self.replay_size, self.num_of_actions), dtype=np.float32)))
loss = F.mean_squared_error(td_clip, zero_val)
return loss, Q
def stockExperience(self, time,
state, action, lstm_reward, state_dash,
episode_end_flag, ale_reward):
data_index = time % self.data_size
if episode_end_flag is True:
self.D[0][data_index] = state
self.D[1][data_index] = action
self.D[2][data_index] = lstm_reward
self.D[5][data_index] = ale_reward
else:
self.D[0][data_index] = state
self.D[1][data_index] = action
self.D[2][data_index] = lstm_reward
self.D[3][data_index] = state_dash
self.D[5][data_index] = ale_reward
self.D[4][data_index] = episode_end_flag
def experienceReplay(self, time):
if self.initial_exploration < time:
# Pick up replay_size number of samples from the Data
if time < self.data_size: # during the first sweep of the History Data
replay_index = np.random.randint(0, time, (self.replay_size, 1))
else:
replay_index = np.random.randint(0, self.data_size, (self.replay_size, 1))
s_replay = np.ndarray(shape=(self.replay_size, 4, 84, 84), dtype=np.float32)
a_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.uint8)
r_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.float32)
s_dash_replay = np.ndarray(shape=(self.replay_size, 4, 84, 84), dtype=np.float32)
episode_end_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.bool)
for i in xrange(self.replay_size):
s_replay[i] = np.asarray(self.D[0][replay_index[i]], dtype=np.float32)
a_replay[i] = self.D[1][replay_index[i]]
r_replay[i] = self.D[2][replay_index[i]]
s_dash_replay[i] = np.array(self.D[3][replay_index[i]], dtype=np.float32)
episode_end_replay[i] = self.D[4][replay_index[i]]
s_replay = cuda.to_gpu(s_replay)
s_dash_replay = cuda.to_gpu(s_dash_replay)
# Gradient-based update
self.optimizer.zero_grads()
loss, _ = self.forward(s_replay, a_replay, r_replay, s_dash_replay, episode_end_replay)
loss.backward()
self.optimizer.update()
def Q_func(self, state):
h1 = F.relu(self.CNN_model.l1(state / 254.0)) # scale inputs in [0.0 1.0]
h2 = F.relu(self.CNN_model.l2(h1))
h3 = F.relu(self.CNN_model.l3(h2))
h4 = F.relu(self.model.l4(h3))
#test now
#print h3.data.shape
Q = self.model.q_value(h4)
return Q
def Q_func_LSTM(self, state):
h1 = F.relu(self.CNN_model.l1(state / 254.0)) # scale inputs in [0.0 1.0]
h2 = F.relu(self.CNN_model.l2(h1))
h3 = F.relu(self.CNN_model.l3(h2))
return h3.data.get()
def Q_func_target(self, state):
h1 = F.relu(self.CNN_model_target.l1(state / 254.0)) # scale inputs in [0.0 1.0]
h2 = F.relu(self.CNN_model_target.l2(h1))
h3 = F.relu(self.CNN_model_target.l3(h2))
h4 = F.relu(self.model.l4(h3))
Q = self.model_target.q_value(h4)
return Q
def LSTM_reward(self, lstm_out, state_next):
lstm_reward = np.sign((self.lstm_loss - (lstm_out - state_next)**2))
return lstm_reward
def e_greedy(self, state, epsilon):
s = Variable(state)
Q = self.Q_func(s)
Q = Q.data
if np.random.rand() < epsilon:
index_action = np.random.randint(0, self.num_of_actions)
print "RANDOM"
else:
index_action = np.argmax(Q.get())
print "GREEDY"
return self.index_to_action(index_action), Q
def target_model_update(self):
self.model_target = copy.deepcopy(self.model)
def index_to_action(self, index_of_action):
return self.enable_controller[index_of_action]
def action_to_index(self, action):
return self.enable_controller.index(action)
# Neural net architecture
# ニューラルネットの構造
def forward(x_data, y_data, train=True):
x, t = Variable(x_data), Variable(y_data)
h1 = F.dropout(F.relu(model.l1(x)), train=train)
h2 = F.dropout(F.relu(model.l2(h1)), train=train)
y = model.l3(h2)
# 多クラス分類なので誤差関数としてソフトマックス関数の
# 交差エントロピー関数を用いて、誤差を導出
return F.softmax_cross_entropy(y, t), F.accuracy(y, t)
# Setup optimizer
optimizer = optimizers.Adam()
optimizer.setup(model.collect_parameters())
train_loss = []
train_acc = []
test_loss = []
test_acc = []
l1_W = []
l2_W = []
l3_W = []
# Learning loop
for epoch in xrange(1, n_epoch + 1):
print 'epoch', epoch
# training
class QNet:
# Hyper-Parameters
gamma = 0.99 # Discount factor
initial_exploration = 10**3 # Initial exploratoin. original: 5x10^4
replay_size = 32 # Replay (batch) size
target_model_update_freq = 10**4 # Target update frequancy. original: 10^4
data_size = 10**5 # Data size of history. original: 10^6
hist_size = 4
def __init__(self, use_gpu, num_of_action_type, num_of_pad, dim):
self.use_gpu = use_gpu
self.num_of_action_type = num_of_action_type
self.num_of_pad = num_of_pad
self.num_of_actions = num_of_action_type * num_of_pad
self.dim = dim
print("Initializing Q-Network...\n")
self.q_net_filename = "q_net.pickle"
if os.path.exists(self.q_net_filename):
print("Loading Q-Network Model...\n")
self.model = self.load_model()
else:
hidden_dim = 256
self.model = FunctionSet(l4=F.Linear(self.dim * self.hist_size,
hidden_dim,
wscale=np.sqrt(2)),
q_value=F.Linear(
hidden_dim,
self.num_of_actions,
initialW=np.zeros(
(self.num_of_actions, hidden_dim),
dtype=np.float32)))
if self.use_gpu >= 0:
self.model.to_gpu()
self.model_target = copy.deepcopy(self.model)
self.optimizer = optimizers.RMSpropGraves(lr=0.00025,
alpha=0.95,
momentum=0.95,
eps=0.0001)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.d = [
np.zeros((self.data_size, self.hist_size, self.dim),
dtype=np.uint8),
np.zeros((self.data_size, self.num_of_pad), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, self.hist_size, self.dim),
dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)
]
def load_model(self):
return pickle.load(open(self.q_net_filename, 'rb'))
def dump_model(self):
pickle.dump(self.model, open(self.q_net_filename, 'wb'))
def forward(self, state, action, reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
q = self.q_func(s) # Get Q-value
# Generate Target Signals
tmp = self.q_func_target(s_dash) # Q(s',*)
if self.use_gpu >= 0:
tmp = list(map(np.max, tmp.data.get())) # max_a Q(s',a)
else:
tmp = list(map(np.max, tmp.data)) # max_a Q(s',a)
max_q_dash = np.asanyarray(tmp, dtype=np.float32)
if self.use_gpu >= 0:
target = np.asanyarray(q.data.get(), dtype=np.float32)
else:
# make new array
target = np.array(q.data, dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = reward[i] + self.gamma * max_q_dash[i]
else:
tmp_ = reward[i]
action_indexes = self.action_to_indexes(action[i])
for index in action_indexes:
target[i, index] = tmp_
# TD-error clipping
if self.use_gpu >= 0:
target = cuda.to_gpu(target)
td = Variable(target) - q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td / abs(td_tmp) * (abs(td.data) >
1)
zero_val = np.zeros((self.replay_size, self.num_of_actions),
dtype=np.float32)
if self.use_gpu >= 0:
zero_val = cuda.to_gpu(zero_val)
zero_val = Variable(zero_val)
loss = F.mean_squared_error(td_clip, zero_val)
return loss, q
def stock_experience(self, time, state, action, reward, state_dash,
episode_end_flag):
data_index = time % self.data_size
if episode_end_flag is True:
self.d[0][data_index] = state
self.d[1][data_index] = action
self.d[2][data_index] = reward
else:
self.d[0][data_index] = state
self.d[1][data_index] = action
self.d[2][data_index] = reward
self.d[3][data_index] = state_dash
self.d[4][data_index] = episode_end_flag
def experience_replay(self, time):
if self.initial_exploration < time:
# Pick up replay_size number of samples from the Data
if time < self.data_size: # during the first sweep of the History Data
replay_index = np.random.randint(0, time,
(self.replay_size, 1))
else:
replay_index = np.random.randint(0, self.data_size,
(self.replay_size, 1))
s_replay = np.ndarray(shape=(self.replay_size, self.hist_size,
self.dim),
dtype=np.float32)
a_replay = np.ndarray(shape=(self.replay_size, self.num_of_pad),
dtype=np.uint8)
r_replay = np.ndarray(shape=(self.replay_size, 1),
dtype=np.float32)
s_dash_replay = np.ndarray(shape=(self.replay_size, self.hist_size,
self.dim),
dtype=np.float32)
episode_end_replay = np.ndarray(shape=(self.replay_size, 1),
dtype=np.bool)
for i in xrange(self.replay_size):
s_replay[i] = np.asarray(self.d[0][replay_index[i]],
dtype=np.float32)
a_replay[i] = self.d[1][replay_index[i]]
r_replay[i] = self.d[2][replay_index[i]]
s_dash_replay[i] = np.array(self.d[3][replay_index[i]],
dtype=np.float32)
episode_end_replay[i] = self.d[4][replay_index[i]]
if self.use_gpu >= 0:
s_replay = cuda.to_gpu(s_replay)
s_dash_replay = cuda.to_gpu(s_dash_replay)
# Gradient-based update
self.optimizer.zero_grads()
loss, _ = self.forward(s_replay, a_replay, r_replay, s_dash_replay,
episode_end_replay)
loss.backward()
self.optimizer.update()
def q_func(self, state):
h4 = F.relu(self.model.l4(state))
q = self.model.q_value(h4 / 255.0)
return q
def q_func_target(self, state):
h4 = F.relu(self.model_target.l4(state / 255.0))
q = self.model_target.q_value(h4)
return q
def e_greedy(self, state, epsilon):
s = Variable(state)
q = self.q_func(s)
q = q.data
if np.random.rand() < epsilon:
print(" Random")
action = [
np.random.randint(0, self.num_of_action_type)
for i in range(self.num_of_pad)
]
else:
print("#Greedy")
if self.use_gpu >= 0:
action = self.indexes_to_action([
np.argmax(sq)
for sq in np.split(q.get()[0], self.num_of_pad)
])
else:
action = self.indexes_to_action(
[np.argmax(sq) for sq in np.split(q[0], self.num_of_pad)])
return action, q
def target_model_update(self):
self.model_target = copy.deepcopy(self.model)
def indexes_to_action(self, indexs_of_action):
return [index % self.num_of_action_type for index in indexs_of_action]
def action_to_indexes(self, action):
return [
self.num_of_action_type * i + a for (i, a) in enumerate(action)
]
def Main():
import argparse
import numpy as np
from chainer import cuda, Variable, FunctionSet, optimizers
import chainer.functions as F
parser = argparse.ArgumentParser(description='Chainer example: regression')
parser.add_argument('--gpu', '-g', default=-1, type=int,
help='GPU ID (negative value indicates CPU)')
args = parser.parse_args()
batchsize = 10
n_epoch = NEpoch
n_units = 300 #TEST
# Prepare dataset
data_x, data_y = LoadData()
batchsize= max(1,min(batchsize, len(data_y)/20)) #TEST: adjust batchsize
#dx2,dy2=GenData(300, noise=0.0); data_x.extend(dx2); data_y.extend(dy2)
data = np.array(data_x).astype(np.float32)
target = np.array(data_y).astype(np.int32) #DIFF_REG
N= len(data) #batchsize * 30
x_train= data
y_train= target
#For test:
mi,ma,me= GetStat(data_x)
f_reduce=lambda xa:[xa[0],xa[1]]
f_repair=lambda xa:[xa[0],xa[1]]
nt= 20+1
N_test= nt*nt
x_test= np.array(sum([[f_repair([x1,x2]) for x2 in FRange1(f_reduce(mi)[1],f_reduce(ma)[1],nt)] for x1 in FRange1(f_reduce(mi)[0],f_reduce(ma)[0],nt)],[])).astype(np.float32)
y_test= np.array([0.0 for x in x_test]).astype(np.int32) #DIFF_REG
#No true test data (just for plotting)
print 'Num of samples for train:',len(y_train),'batchsize:',batchsize
# Dump data for plot:
DumpData('/tmp/nn/smpl_train.dat', x_train, [[y] for y in y_train], f_reduce) #DIFF_REG
# Prepare multi-layer perceptron model
model = FunctionSet(l1=F.Linear(2, n_units),
l2=F.Linear(n_units, n_units),
l3=F.Linear(n_units, 3))
#TEST: Random bias initialization
#, bias=Rand()
#model.l1.b[:]= [Rand() for k in range(n_units)]
#model.l2.b[:]= [Rand() for k in range(n_units)]
#model.l3.b[:]= [Rand() for k in range(1)]
#print model.l2.__dict__
if args.gpu >= 0:
cuda.init(args.gpu)
model.to_gpu()
# Neural net architecture
def forward(x_data, y_data, train=True):
#train= False #TEST: Turn off dropout
dratio= 0.2 #0.5 #TEST: Dropout ratio
x, t = Variable(x_data), Variable(y_data)
h1 = F.dropout(F.relu(model.l1(x)), ratio=dratio, train=train)
h2 = F.dropout(F.relu(model.l2(h1)), ratio=dratio, train=train)
#h1 = F.dropout(F.leaky_relu(model.l1(x),slope=0.2), ratio=dratio, train=train)
#h2 = F.dropout(F.leaky_relu(model.l2(h1),slope=0.2), ratio=dratio, train=train)
#h1 = F.dropout(F.sigmoid(model.l1(x)), ratio=dratio, train=train)
#h2 = F.dropout(F.sigmoid(model.l2(h1)), ratio=dratio, train=train)
#h1 = F.dropout(F.tanh(model.l1(x)), ratio=dratio, train=train)
#h2 = F.dropout(F.tanh(model.l2(h1)), ratio=dratio, train=train)
#h1 = F.dropout(model.l1(x), ratio=dratio, train=train)
#h2 = F.dropout(model.l2(h1), ratio=dratio, train=train)
#h1 = F.relu(model.l1(x))
#h2 = F.relu(model.l2(h1))
#h1 = model.l1(x)
#h2 = model.l2(h1)
y = model.l3(h2)
#return F.mean_squared_error(y, t), y
return F.softmax_cross_entropy(y, t), F.softmax(y) #DIFF_REG
# Setup optimizer
optimizer = optimizers.AdaDelta(rho=0.9)
#optimizer = optimizers.AdaGrad(lr=0.5)
#optimizer = optimizers.RMSprop()
#optimizer = optimizers.MomentumSGD()
#optimizer = optimizers.SGD(lr=0.8)
optimizer.setup(model.collect_parameters())
# Learning loop
for epoch in xrange(1, n_epoch+1):
print 'epoch', epoch
# training
perm = np.random.permutation(N)
sum_loss = 0
for i in xrange(0, N, batchsize):
x_batch = x_train[perm[i:i+batchsize]]
y_batch = y_train[perm[i:i+batchsize]]
if args.gpu >= 0:
x_batch = cuda.to_gpu(x_batch)
y_batch = cuda.to_gpu(y_batch)
optimizer.zero_grads()
loss, pred = forward(x_batch, y_batch)
loss.backward() #Computing gradients
optimizer.update()
sum_loss += float(cuda.to_cpu(loss.data)) * batchsize
print 'train mean loss={}'.format(
sum_loss / N)
if epoch%10==0:
#'''
# testing all data
preds = []
x_batch = x_test[:]
y_batch = y_test[:]
if args.gpu >= 0:
x_batch = cuda.to_gpu(x_batch)
y_batch = cuda.to_gpu(y_batch)
loss, pred = forward(x_batch, y_batch, train=False)
preds = cuda.to_cpu(pred.data)
sum_loss = float(cuda.to_cpu(loss.data)) * len(y_test)
#'''
print 'test mean loss={}'.format(
sum_loss / N_test)
# Dump data for plot:
y_pred= [[y.index(max(y))]+y for y in preds.tolist()] #DIFF_REG
DumpData('/tmp/nn/nn_test%04i.dat'%epoch, x_test, y_pred, f_reduce, lb=nt+1)
class QNet:
# Hyper-Parameters
gamma = 0.99 # Discount factor
initial_exploration = 10**3 # Initial exploratoin. original: 5x10^4
replay_size = 32 # Replay (batch) size
target_model_update_freq = 10**4 # Target update frequancy. original: 10^4
data_size = 10**5 # Data size of history. original: 10^6
hist_size = 1 # original: 4
def __init__(self, use_gpu, enable_controller, dim, epsilon, epsilon_delta, min_eps):
self.use_gpu = use_gpu
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller
self.dim = dim
self.epsilon = epsilon
self.epsilon_delta = epsilon_delta
self.min_eps = min_eps
self.time = 0
app_logger.info("Initializing Q-Network...")
hidden_dim = 256
self.model = FunctionSet(
l4=F.Linear(self.dim*self.hist_size, hidden_dim, wscale=np.sqrt(2)),
q_value=F.Linear(hidden_dim, self.num_of_actions,
initialW=np.zeros((self.num_of_actions, hidden_dim),
dtype=np.float32))
)
if self.use_gpu >= 0:
self.model.to_gpu()
self.model_target = copy.deepcopy(self.model)
self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.0001)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.d = [np.zeros((self.data_size, self.hist_size, self.dim), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, self.hist_size, self.dim), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)]
def forward(self, state, action, reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
q = self.q_func(s) # Get Q-value
# Generate Target Signals
tmp = self.q_func_target(s_dash) # Q(s',*)
if self.use_gpu >= 0:
tmp = list(map(np.max, tmp.data.get())) # max_a Q(s',a)
else:
tmp = list(map(np.max, tmp.data)) # max_a Q(s',a)
max_q_dash = np.asanyarray(tmp, dtype=np.float32)
if self.use_gpu >= 0:
target = np.asanyarray(q.data.get(), dtype=np.float32)
else:
# make new array
target = np.array(q.data, dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = reward[i] + self.gamma * max_q_dash[i]
else:
tmp_ = reward[i]
action_index = self.action_to_index(action[i])
target[i, action_index] = tmp_
# TD-error clipping
if self.use_gpu >= 0:
target = cuda.to_gpu(target)
td = Variable(target) - q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td/abs(td_tmp) * (abs(td.data) > 1)
zero_val = np.zeros((self.replay_size, self.num_of_actions), dtype=np.float32)
if self.use_gpu >= 0:
zero_val = cuda.to_gpu(zero_val)
zero_val = Variable(zero_val)
loss = F.mean_squared_error(td_clip, zero_val)
return loss, q
def q_func(self, state):
h4 = F.relu(self.model.l4(state / 255.0))
q = self.model.q_value(h4)
return q
def q_func_target(self, state):
h4 = F.relu(self.model_target.l4(state / 255.0))
q = self.model_target.q_value(h4)
return q
def e_greedy(self, state, epsilon):
s = Variable(state)
q = self.q_func(s)
q = q.data
if np.random.rand() < epsilon:
index_action = np.random.randint(0, self.num_of_actions)
app_logger.info(" Random")
else:
if self.use_gpu >= 0:
index_action = np.argmax(q.get())
else:
index_action = np.argmax(q)
app_logger.info("#Greedy")
return self.index_to_action(index_action), q
def target_model_update(self):
self.model_target = copy.deepcopy(self.model)
def index_to_action(self, index_of_action):
return self.enable_controller[index_of_action]
def action_to_index(self, action):
return self.enable_controller.index(action)
def start(self, feature):
self.state = np.zeros((self.hist_size, self.dim), dtype=np.uint8)
self.state[0] = feature
state_ = np.asanyarray(self.state.reshape(1, self.hist_size, self.dim), dtype=np.float32)
if self.use_gpu >= 0:
state_ = cuda.to_gpu(state_)
# Generate an Action e-greedy
action, q_now = self.e_greedy(state_, self.epsilon)
return_action = action
return return_action
def update_model(self, replayed_experience):
if replayed_experience[0]:
self.optimizer.zero_grads()
loss, _ = self.forward(replayed_experience[1], replayed_experience[2],
replayed_experience[3], replayed_experience[4], replayed_experience[5])
loss.backward()
self.optimizer.update()
# Target model update
if replayed_experience[0] and np.mod(self.time, self.target_model_update_freq) == 0:
app_logger.info("Model Updated")
self.target_model_update()
self.time += 1
app_logger.info("step: {}".format(self.time))
def step(self, features):
if self.hist_size == 4:
self.state = np.asanyarray([self.state[1], self.state[2], self.state[3], features], dtype=np.uint8)
elif self.hist_size == 2:
self.state = np.asanyarray([self.state[1], features], dtype=np.uint8)
elif self.hist_size == 1:
self.state = np.asanyarray([features], dtype=np.uint8)
else:
app_logger.error("self.DQN.hist_size err")
state_ = np.asanyarray(self.state.reshape(1, self.hist_size, self.dim), dtype=np.float32)
if self.use_gpu >= 0:
state_ = cuda.to_gpu(state_)
# Exploration decays along the time sequence
if self.initial_exploration < self.time:
self.epsilon -= self.epsilon_delta
if self.epsilon < self.min_eps:
self.epsilon = self.min_eps
eps = self.epsilon
else: # Initial Exploation Phase
app_logger.info("Initial Exploration : {}/{} steps".format(self.time, self.initial_exploration))
eps = 1.0
# Generate an Action by e-greedy action selection
action, q_now = self.e_greedy(state_, eps)
if self.use_gpu >= 0:
q_max = np.max(q_now.get())
else:
q_max = np.max(q_now)
return action, eps, q_max
class ConvolutionalDenoisingAutoencoder():
def __init__(self,
imgsize,
n_in_channels,
n_out_channels,
ksize,
stride=1,
pad=0,
use_cuda=False):
self.model = FunctionSet(
encode=F.Convolution2D(n_in_channels, n_out_channels, ksize,
stride, pad),
decode=F.Linear(
n_out_channels * (math.floor(
(imgsize + 2 * pad - ksize) / stride) + 1)**2,
n_in_channels * imgsize**2))
self.use_cuda = use_cuda
if self.use_cuda:
self.model.to_gpu()
self.optimizer = optimizers.Adam()
self.optimizer.setup(self.model.collect_parameters())
def encode(self, x_var):
return F.sigmoid(self.model.encode(x_var))
def decode(self, x_var):
return self.model.decode(x_var)
def predict(self, x_data):
if self.use_cuda:
x_data = cuda.to_gpu(x_data)
x = Variable(x_data)
p = self.encode(x)
if self.use_cuda:
return cuda.to_cpu(p.data)
else:
return p.data
def cost(self, x_data):
x = Variable(x_data)
t = Variable(
x_data.reshape(x_data.shape[0], x_data.shape[1] * x_data.shape[2] *
x_data.shape[3]))
h = F.dropout(x)
h = self.encode(h)
y = self.decode(h)
return F.mean_squared_error(y, t)
def train(self, x_data):
if self.use_cuda:
x_data = cuda.to_gpu(x_data)
self.optimizer.zero_grads()
loss = self.cost(x_data)
loss.backward()
self.optimizer.update()
if self.use_cuda:
return float(cuda.to_cpu(loss.data))
else:
return loss.data
def test(self, x_data):
if self.use_cuda:
x_data = cuda.to_gpu(x_data)
loss = self.cost(x_data)
return float(cuda.to_cpu(loss.data))
# Prepare multi-layer perceptron model
model = FunctionSet(l1=F.Linear(784, n_units),
l2=F.Linear(n_units, n_units),
l3=F.Linear(n_units, 10))
# Neural net architecture
def forward(x_data, y_data, train=True):
x, t = Variable(x_data), Variable(y_data)
h1 = F.dropout(F.relu(model.l1(x)), train=train)
h2 = F.dropout(F.relu(model.l2(h1)), train=train)
y = model.l3(h2)
return F.softmax_cross_entropy(y, t), F.accuracy(y, t)
# Setup optimizer
optimizer = optimizers.Adam()
optimizer.setup(model.collect_parameters())
# Learning loop
for epoch in xrange(1, n_epoch+1):
print 'epoch', epoch
# training
perm = np.random.permutation(N)
sum_accuracy = 0
sum_loss = 0
for i in xrange(0, N, batchsize):
x_batch = x_train[perm[i:i+batchsize]]
y_batch = y_train[perm[i:i+batchsize]]
optimizer.zero_grads()
loss, acc = forward(x_batch, y_batch)
loss.backward()
optimizer.update()
sum_loss += float(loss.data) * batchsize
class DQN_class:
# Hyper-Parameters
gamma = 0.99 # Discount factor
initial_exploration = 100 #10**4 # Initial exploratoin. original: 5x10^4
replay_size = 32 # Replay (batch) size
target_model_update_freq = 10**4 # Target update frequancy. original: 10^4
data_size = 10**5 #10**5 # Data size of history. original: 10^6
def __init__(self, enable_controller=[0, 3, 4]):
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller # Default setting : "Pong"
print "Initializing DQN..."
print "Model Building"
self.CNN_model = FunctionSet(
l1=F.Convolution2D(4,
32,
ksize=8,
stride=4,
nobias=False,
wscale=np.sqrt(2)),
l2=F.Convolution2D(32,
64,
ksize=4,
stride=2,
nobias=False,
wscale=np.sqrt(2)),
l3=F.Convolution2D(64,
64,
ksize=3,
stride=1,
nobias=False,
wscale=np.sqrt(2)),
)
self.model = FunctionSet(
l4=F.Linear(3136, 512, wscale=np.sqrt(2)),
q_value=F.Linear(512,
self.num_of_actions,
initialW=np.zeros((self.num_of_actions, 512),
dtype=np.float32))).to_gpu()
d = 'elite/'
self.CNN_model.l1.W.data = np.load(d +
'l1_W.npy') #.astype(np.float32)
self.CNN_model.l1.b.data = np.load(d +
'l1_b.npy') #.astype(np.float32)
self.CNN_model.l2.W.data = np.load(d +
'l2_W.npy') #.astype(np.float32)
self.CNN_model.l2.b.data = np.load(d +
'l2_b.npy') #.astype(np.float32)
self.CNN_model.l3.W.data = np.load(d +
'l3_W.npy') #.astype(np.float32)
self.CNN_model.l3.b.data = np.load(d +
'l3_b.npy') #.astype(np.float32)
self.CNN_model = self.CNN_model.to_gpu()
self.CNN_model_target = copy.deepcopy(self.CNN_model)
self.model_target = copy.deepcopy(self.model)
print "Initizlizing Optimizer"
self.optimizer = optimizers.RMSpropGraves(lr=0.00025,
alpha=0.95,
momentum=0.95,
eps=0.0001)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.D = [
np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool),
np.zeros((self.data_size, 1), dtype=np.uint8)
]
def forward(self, state, action, Reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
Q = self.Q_func(s) # Get Q-value
# Generate Target Signals
tmp = self.Q_func_target(s_dash) # Q(s',*)
tmp = list(map(np.max, tmp.data.get())) # max_a Q(s',a)
max_Q_dash = np.asanyarray(tmp, dtype=np.float32)
target = np.asanyarray(Q.data.get(), dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = np.sign(Reward[i]) + self.gamma * max_Q_dash[i]
else:
tmp_ = np.sign(Reward[i])
action_index = self.action_to_index(action[i])
target[i, action_index] = tmp_
# TD-error clipping
td = Variable(cuda.to_gpu(target)) - Q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td / abs(td_tmp) * (abs(td.data) >
1)
zero_val = Variable(
cuda.to_gpu(
np.zeros((self.replay_size, self.num_of_actions),
dtype=np.float32)))
loss = F.mean_squared_error(td_clip, zero_val)
return loss, Q
def stockExperience(self, time, state, action, lstm_reward, state_dash,
episode_end_flag, ale_reward):
data_index = time % self.data_size
if episode_end_flag is True:
self.D[0][data_index] = state
self.D[1][data_index] = action
self.D[2][data_index] = lstm_reward
self.D[5][data_index] = ale_reward
else:
self.D[0][data_index] = state
self.D[1][data_index] = action
self.D[2][data_index] = lstm_reward
self.D[3][data_index] = state_dash
self.D[5][data_index] = ale_reward
self.D[4][data_index] = episode_end_flag
def experienceReplay(self, time):
if self.initial_exploration < time:
# Pick up replay_size number of samples from the Data
if time < self.data_size: # during the first sweep of the History Data
replay_index = np.random.randint(0, time,
(self.replay_size, 1))
else:
replay_index = np.random.randint(0, self.data_size,
(self.replay_size, 1))
s_replay = np.ndarray(shape=(self.replay_size, 4, 84, 84),
dtype=np.float32)
a_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.uint8)
r_replay = np.ndarray(shape=(self.replay_size, 1),
dtype=np.float32)
s_dash_replay = np.ndarray(shape=(self.replay_size, 4, 84, 84),
dtype=np.float32)
episode_end_replay = np.ndarray(shape=(self.replay_size, 1),
dtype=np.bool)
for i in xrange(self.replay_size):
s_replay[i] = np.asarray(self.D[0][replay_index[i]],
dtype=np.float32)
a_replay[i] = self.D[1][replay_index[i]]
r_replay[i] = self.D[2][replay_index[i]]
s_dash_replay[i] = np.array(self.D[3][replay_index[i]],
dtype=np.float32)
episode_end_replay[i] = self.D[4][replay_index[i]]
s_replay = cuda.to_gpu(s_replay)
s_dash_replay = cuda.to_gpu(s_dash_replay)
# Gradient-based update
self.optimizer.zero_grads()
loss, _ = self.forward(s_replay, a_replay, r_replay, s_dash_replay,
episode_end_replay)
loss.backward()
self.optimizer.update()
def Q_func(self, state):
h1 = F.relu(self.CNN_model.l1(state /
254.0)) # scale inputs in [0.0 1.0]
h2 = F.relu(self.CNN_model.l2(h1))
h3 = F.relu(self.CNN_model.l3(h2))
h4 = F.relu(self.model.l4(h3))
#test now
#print h3.data.shape
Q = self.model.q_value(h4)
return Q
def Q_func_LSTM(self, state):
h1 = F.relu(self.CNN_model.l1(state /
254.0)) # scale inputs in [0.0 1.0]
h2 = F.relu(self.CNN_model.l2(h1))
h3 = F.relu(self.CNN_model.l3(h2))
return h3.data.get()
def Q_func_target(self, state):
h1 = F.relu(self.CNN_model_target.l1(
state / 254.0)) # scale inputs in [0.0 1.0]
h2 = F.relu(self.CNN_model_target.l2(h1))
h3 = F.relu(self.CNN_model_target.l3(h2))
h4 = F.relu(self.model.l4(h3))
Q = self.model_target.q_value(h4)
return Q
def LSTM_reward(self, lstm_out, state_next):
lstm_reward = np.sign((self.lstm_loss - (lstm_out - state_next)**2))
return lstm_reward
def e_greedy(self, state, epsilon):
s = Variable(state)
Q = self.Q_func(s)
Q = Q.data
if np.random.rand() < epsilon:
index_action = np.random.randint(0, self.num_of_actions)
print "RANDOM"
else:
index_action = np.argmax(Q.get())
print "GREEDY"
return self.index_to_action(index_action), Q
def target_model_update(self):
self.model_target = copy.deepcopy(self.model)
def index_to_action(self, index_of_action):
return self.enable_controller[index_of_action]
def action_to_index(self, action):
return self.enable_controller.index(action)
class QNet:
# Hyper-Parameters
gamma = 0.99 # Discount factor
initial_exploration = 10**3 # Initial exploratoin. original: 5x10^4
replay_size = 32 # Replay (batch) size
target_model_update_freq = 10**4 # Target update frequancy. original: 10^4
data_size = 10**5 # Data size of history. original: 10^6
hist_size = 1 #original: 4
def __init__(self, use_gpu, enable_controller, dim):
self.use_gpu = use_gpu
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller
self.dim = dim
print("Initializing Q-Network...")
#hidden_dim = 256
hidden_dim128 = 128
self.model = FunctionSet(
l4=F.Linear(self.dim * self.hist_size,
hidden_dim128,
wscale=np.sqrt(2)),
l5=F.Linear(self.dim * self.hist_size,
hidden_dim128,
wscale=np.sqrt(2)),
l6=F.Linear(hidden_dim128,
1,
wscale=np.sqrt(2),
initialW=np.zeros((1, hidden_dim128),
dtype=np.float32)), #V(s,a)
l7=F.Linear(hidden_dim128,
self.num_of_actions,
wscale=np.sqrt(2),
initialW=np.zeros((self.num_of_actions, hidden_dim128),
dtype=np.float32)), #A(a)
q_value=DN_out.DN_out(1,
self.num_of_actions,
self.num_of_actions,
nobias=True))
if self.use_gpu >= 0:
self.model.to_gpu()
self.model_target = copy.deepcopy(self.model)
self.optimizer = optimizers.RMSpropGraves(lr=0.00025,
alpha=0.95,
momentum=0.95,
eps=0.0001)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.d = [
np.zeros((self.data_size, self.hist_size, self.dim),
dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, self.hist_size, self.dim),
dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)
]
def forward(self, state, action, reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
q = self.q_func(s) # Get Q-value
# Generate Target Signals
tmp = self.q_func_target(s_dash) # Q(s',*)
if self.use_gpu >= 0:
tmp = list(map(np.max, tmp.data.get())) # max_a Q(s',a)
else:
tmp = list(map(np.max, tmp.data)) # max_a Q(s',a)
max_q_dash = np.asanyarray(tmp, dtype=np.float32)
if self.use_gpu >= 0:
target = np.asanyarray(q.data.get(), dtype=np.float32)
else:
# make new array
target = np.array(q.data, dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = reward[i] + self.gamma * max_q_dash[i]
else:
tmp_ = reward[i]
action_index = self.action_to_index(action[i])
target[i, action_index] = tmp_
# TD-error clipping
if self.use_gpu >= 0:
target = cuda.to_gpu(target)
td = Variable(target) - q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td / abs(td_tmp) * (abs(td.data) >
1)
zero_val = np.zeros((self.replay_size, self.num_of_actions),
dtype=np.float32)
if self.use_gpu >= 0:
zero_val = cuda.to_gpu(zero_val)
zero_val = Variable(zero_val)
loss = F.mean_squared_error(td_clip, zero_val)
return loss, q
def stock_experience(self, time, state, action, reward, state_dash,
episode_end_flag):
data_index = time % self.data_size
if episode_end_flag is True:
self.d[0][data_index] = state
self.d[1][data_index] = action
self.d[2][data_index] = reward
else:
self.d[0][data_index] = state
self.d[1][data_index] = action
self.d[2][data_index] = reward
self.d[3][data_index] = state_dash
self.d[4][data_index] = episode_end_flag
def experience_replay(self, time):
if self.initial_exploration < time:
# Pick up replay_size number of samples from the Data
if time < self.data_size: # during the first sweep of the History Data
replay_index = np.random.randint(0, time,
(self.replay_size, 1))
else:
replay_index = np.random.randint(0, self.data_size,
(self.replay_size, 1))
s_replay = np.ndarray(shape=(self.replay_size, self.hist_size,
self.dim),
dtype=np.float32)
a_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.uint8)
r_replay = np.ndarray(shape=(self.replay_size, 1),
dtype=np.float32)
s_dash_replay = np.ndarray(shape=(self.replay_size, self.hist_size,
self.dim),
dtype=np.float32)
episode_end_replay = np.ndarray(shape=(self.replay_size, 1),
dtype=np.bool)
for i in xrange(self.replay_size):
s_replay[i] = np.asarray(self.d[0][replay_index[i]],
dtype=np.float32)
a_replay[i] = self.d[1][replay_index[i]]
r_replay[i] = self.d[2][replay_index[i]]
s_dash_replay[i] = np.array(self.d[3][replay_index[i]],
dtype=np.float32)
episode_end_replay[i] = self.d[4][replay_index[i]]
if self.use_gpu >= 0:
s_replay = cuda.to_gpu(s_replay)
s_dash_replay = cuda.to_gpu(s_dash_replay)
# Gradient-based update
self.optimizer.zero_grads()
loss, _ = self.forward(s_replay, a_replay, r_replay, s_dash_replay,
episode_end_replay)
loss.backward()
self.optimizer.update()
def q_func(self, state):
h4 = F.relu(self.model.l4(state / 255.0))
h5 = F.relu(self.model.l5(state / 255.0))
#h6 = F.relu(self.model.l6(h4))
#h7 = relu_l7.relu(self.model.l7(h5))
h6 = self.model.l6(h4)
h7 = self.model.l7(h5)
q = self.model.q_value(h6, h7)
return q
def q_func_target(self, state):
#h4 = F.relu(self.model_target.l4(state / 255.0))
#q = self.model_target.q_value(h4)
h4 = F.relu(self.model_target.l4(state / 255.0))
h5 = F.relu(self.model_target.l5(state / 255.0))
#h6 = F.relu(self.model_target.l6(h4))
#h7 = relu_l7.relu(self.model_target.l7(h5))
h6 = self.model_target.l6(h4)
h7 = self.model_target.l7(h5)
q = self.model_target.q_value(h6, h7)
return q
def e_greedy(self, state, epsilon):
s = Variable(state)
q = self.q_func(s)
q = q.data
if np.random.rand() < epsilon:
index_action = np.random.randint(0, self.num_of_actions)
print(" Random"),
else:
if self.use_gpu >= 0:
index_action = np.argmax(q.get())
else:
index_action = np.argmax(q)
print("#Greedy"),
return self.index_to_action(index_action), q
def target_model_update(self):
self.model_target = copy.deepcopy(self.model)
def index_to_action(self, index_of_action):
return self.enable_controller[index_of_action]
def action_to_index(self, action):
return self.enable_controller.index(action)
def Main():
import argparse
import numpy as np
from sklearn.datasets import load_diabetes
from chainer import cuda, Variable, FunctionSet, optimizers
import chainer.functions as F
parser = argparse.ArgumentParser(description='Chainer example: regression')
parser.add_argument('--gpu',
'-g',
default=-1,
type=int,
help='GPU ID (negative value indicates CPU)')
args = parser.parse_args()
batchsize = 13
n_epoch = 100
n_units = 30
# Prepare dataset
print 'fetch diabetes dataset'
diabetes = load_diabetes()
data = diabetes['data'].astype(np.float32)
target = diabetes['target'].astype(np.float32).reshape(
len(diabetes['target']), 1)
N = batchsize * 30 #Number of training data
x_train, x_test = np.split(data, [N])
y_train, y_test = np.split(target, [N])
N_test = y_test.size
print 'Num of samples for train:', len(y_train)
print 'Num of samples for test:', len(y_test)
# Dump data for plot:
fp1 = file('/tmp/smpl_train.dat', 'w')
for x, y in zip(x_train, y_train):
fp1.write('%s #%i# %s\n' %
(' '.join(map(str, x)), len(x) + 1, ' '.join(map(str, y))))
fp1.close()
# Dump data for plot:
fp1 = file('/tmp/smpl_test.dat', 'w')
for x, y in zip(x_test, y_test):
fp1.write('%s #%i# %s\n' %
(' '.join(map(str, x)), len(x) + 1, ' '.join(map(str, y))))
fp1.close()
# Prepare multi-layer perceptron model
model = FunctionSet(l1=F.Linear(10, n_units),
l2=F.Linear(n_units, n_units),
l3=F.Linear(n_units, 1))
if args.gpu >= 0:
cuda.init(args.gpu)
model.to_gpu()
# Neural net architecture
def forward(x_data, y_data, train=True):
x, t = Variable(x_data), Variable(y_data)
h1 = F.dropout(F.relu(model.l1(x)), train=train)
h2 = F.dropout(F.relu(model.l2(h1)), train=train)
y = model.l3(h2)
return F.mean_squared_error(y, t), y
# Setup optimizer
optimizer = optimizers.AdaDelta(rho=0.9)
optimizer.setup(model.collect_parameters())
# Learning loop
for epoch in xrange(1, n_epoch + 1):
print 'epoch', epoch
# training
perm = np.random.permutation(N)
sum_loss = 0
for i in xrange(0, N, batchsize):
x_batch = x_train[perm[i:i + batchsize]]
y_batch = y_train[perm[i:i + batchsize]]
if args.gpu >= 0:
x_batch = cuda.to_gpu(x_batch)
y_batch = cuda.to_gpu(y_batch)
optimizer.zero_grads()
loss, pred = forward(x_batch, y_batch)
loss.backward()
optimizer.update()
sum_loss += float(cuda.to_cpu(loss.data)) * batchsize
print 'train mean loss={}'.format(sum_loss / N)
'''
# testing per batch
sum_loss = 0
preds = []
for i in xrange(0, N_test, batchsize):
x_batch = x_test[i:i+batchsize]
y_batch = y_test[i:i+batchsize]
if args.gpu >= 0:
x_batch = cuda.to_gpu(x_batch)
y_batch = cuda.to_gpu(y_batch)
loss, pred = forward(x_batch, y_batch, train=False)
preds.extend(cuda.to_cpu(pred.data))
sum_loss += float(cuda.to_cpu(loss.data)) * batchsize
pearson = np.corrcoef(np.asarray(preds).reshape(len(preds),), np.asarray(y_test).reshape(len(preds),))
#'''
#'''
# testing all data
preds = []
x_batch = x_test[:]
y_batch = y_test[:]
if args.gpu >= 0:
x_batch = cuda.to_gpu(x_batch)
y_batch = cuda.to_gpu(y_batch)
loss, pred = forward(x_batch, y_batch, train=False)
preds = cuda.to_cpu(pred.data)
sum_loss = float(cuda.to_cpu(loss.data)) * len(y_test)
pearson = np.corrcoef(
np.asarray(preds).reshape(len(preds), ),
np.asarray(y_test).reshape(len(preds), ))
#'''
print 'test mean loss={}, corrcoef={}'.format(sum_loss / N_test,
pearson[0][1])
# Dump data for plot:
fp1 = file('/tmp/nn_test%04i.dat' % epoch, 'w')
for x, y in zip(x_test, preds):
fp1.write(
'%s #%i# %s\n' %
(' '.join(map(str, x)), len(x) + 1, ' '.join(map(str, y))))
fp1.close()
l1 = da1.model.encoder,
l2 = da2.model.encoder,
l3 = F.Linear(49, 10),
)
# 前向き学習の定義
def forward(x_data, y_data, train=True):
x, t = Variable(x_data), Variable(y_data)
h1 = F.dropout(F.sigmoid(da3.l1(x)), train=train)
h2 = F.dropout(F.sigmoid(da3.l2(h1)), train=train)
y = da3.l3(h2)
return F.softmax_cross_entropy(y, t), F.accuracy(y, t)
# optimizerの定義
optimizer = optimizers.Adam()
optimizer.setup(da3.collect_parameters())
# 学習
n_epoch = 200
all_loss_accuracy = []
for epoch in xrange(n_epoch):
print 'epoch', epoch
indexes = np.random.permutation(N)
loss_accuracy = []
sum_loss, sum_accuracy = 0, 0
for i in xrange(0, N, batchsize):
x_batch = x_train[indexes[i:i+batchsize]]
y_batch = y_train[indexes[i:i+batchsize]]
optimizer.zero_grads()
loss, accuracy = forward(x_batch, y_batch)
loss.backward()
class QNet:
# Hyper-Parameters
gamma = 0.99 # Discount factor
initial_exploration = 10**3 # Initial exploratoin. original: 5x10^4
replay_size = 32 # Replay (batch) size
target_model_update_freq = 10**4 # Target update frequancy. original: 10^4
data_size = 10**5 # Data size of history. original: 10^6
hist_size = 1 #original: 4
def __init__(self, use_gpu, enable_controller, dim):
self.use_gpu = use_gpu
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller
self.dim = dim
print("Initializing Q-Network...")
hidden_dim = 256
self.model = FunctionSet(
l4=F.Linear(self.dim*self.hist_size, hidden_dim, wscale=np.sqrt(2)),
q_value=F.Linear(hidden_dim, self.num_of_actions,
initialW=np.zeros((self.num_of_actions, hidden_dim),
dtype=np.float32))
)
if self.use_gpu >= 0:
self.model.to_gpu()
self.model_target = copy.deepcopy(self.model)
self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.0001)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.d = [np.zeros((self.data_size, self.hist_size, self.dim), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, self.hist_size, self.dim), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)]
def forward(self, state, action, reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
q = self.q_func(s) # Get Q-value
# Generate Target Signals
tmp = self.q_func_target(s_dash) # Q(s',*)
if self.use_gpu >= 0:
tmp = list(map(np.max, tmp.data.get())) # max_a Q(s',a)
else:
tmp = list(map(np.max, tmp.data)) # max_a Q(s',a)
max_q_dash = np.asanyarray(tmp, dtype=np.float32)
if self.use_gpu >= 0:
target = np.asanyarray(q.data.get(), dtype=np.float32)
else:
# make new array
target = np.array(q.data, dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = reward[i] + self.gamma * max_q_dash[i]
else:
tmp_ = reward[i]
action_index = self.action_to_index(action[i])
target[i, action_index] = tmp_
# TD-error clipping
if self.use_gpu >= 0:
target = cuda.to_gpu(target)
td = Variable(target) - q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td/abs(td_tmp) * (abs(td.data) > 1)
zero_val = np.zeros((self.replay_size, self.num_of_actions), dtype=np.float32)
if self.use_gpu >= 0:
zero_val = cuda.to_gpu(zero_val)
zero_val = Variable(zero_val)
loss = F.mean_squared_error(td_clip, zero_val)
return loss, q
def stock_experience(self, time,
state, action, reward, state_dash,
episode_end_flag):
data_index = time % self.data_size
if episode_end_flag is True:
self.d[0][data_index] = state
self.d[1][data_index] = action
self.d[2][data_index] = reward
else:
self.d[0][data_index] = state
self.d[1][data_index] = action
self.d[2][data_index] = reward
self.d[3][data_index] = state_dash
self.d[4][data_index] = episode_end_flag
def experience_replay(self, time):
if self.initial_exploration < time:
# Pick up replay_size number of samples from the Data
if time < self.data_size: # during the first sweep of the History Data
replay_index = np.random.randint(0, time, (self.replay_size, 1))
else:
replay_index = np.random.randint(0, self.data_size, (self.replay_size, 1))
s_replay = np.ndarray(shape=(self.replay_size, self.hist_size, self.dim), dtype=np.float32)
a_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.uint8)
r_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.float32)
s_dash_replay = np.ndarray(shape=(self.replay_size, self.hist_size, self.dim), dtype=np.float32)
episode_end_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.bool)
for i in xrange(self.replay_size):
s_replay[i] = np.asarray(self.d[0][replay_index[i]], dtype=np.float32)
a_replay[i] = self.d[1][replay_index[i]]
r_replay[i] = self.d[2][replay_index[i]]
s_dash_replay[i] = np.array(self.d[3][replay_index[i]], dtype=np.float32)
episode_end_replay[i] = self.d[4][replay_index[i]]
if self.use_gpu >= 0:
s_replay = cuda.to_gpu(s_replay)
s_dash_replay = cuda.to_gpu(s_dash_replay)
# Gradient-based update
self.optimizer.zero_grads()
loss, _ = self.forward(s_replay, a_replay, r_replay, s_dash_replay, episode_end_replay)
loss.backward()
self.optimizer.update()
def q_func(self, state):
h4 = F.relu(self.model.l4(state / 255.0))
q = self.model.q_value(h4)
return q
def q_func_target(self, state):
h4 = F.relu(self.model_target.l4(state / 255.0))
q = self.model_target.q_value(h4)
return q
def e_greedy(self, state, epsilon):
s = Variable(state)
q = self.q_func(s)
q = q.data
if np.random.rand() < epsilon:
index_action = np.random.randint(0, self.num_of_actions)
print(" Random"),
else:
if self.use_gpu >= 0:
index_action = np.argmax(q.get())
else:
index_action = np.argmax(q)
print("#Greedy"),
return self.index_to_action(index_action), q
def target_model_update(self):
self.model_target = copy.deepcopy(self.model)
def index_to_action(self, index_of_action):
return self.enable_controller[index_of_action]
def action_to_index(self, action):
return self.enable_controller.index(action)
class QNet:
# Hyper-Parameters
gamma = 0.99 # Discount factor #-------0.99---0.39--0.99
initial_exploration = 10**4 # Initial exploratoin. original: 5x10^4
replay_size = 32 # Replay (batch) size
target_model_update_freq = 10**4 # Target update frequancy. original: 10^4#------
data_size = 10**5 # Data size of history. original: 10^6
hist_size = 1 #original: 4
def __init__(self, use_gpu, enable_controller, dim):
self.use_gpu = use_gpu
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller
self.dim = dim
print("Initializing Q-Network...")
hidden_dim = 256 # 256--128---256
self.model = FunctionSet(
l4=F.Linear(self.dim * self.hist_size,
hidden_dim,
wscale=np.sqrt(2)), #wscall=np.sqrt(2)---None--
q_value=F.Linear(hidden_dim,
self.num_of_actions,
initialW=np.zeros(
(self.num_of_actions, hidden_dim),
dtype=np.float32)))
if self.use_gpu >= 0:
self.model.to_gpu()
self.model_target = copy.deepcopy(self.model)
#self.optimizer = optimizers.Adam(alpha=0.001, beta1=0.9, beta2=0.999, eps=1e-08)#alpha=0.0015-0.0125-0.005-0.001
self.optimizer = optimizers.RMSpropGraves(lr=0.00025,
alpha=0.95,
momentum=0.95,
eps=0.0001)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]r, int8----------------------------------------
self.d = [
np.zeros((self.data_size, self.hist_size, self.dim),
dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.float32),
np.zeros((self.data_size, self.hist_size, self.dim),
dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)
]
def forward(self, state, action, reward, state_dash, episode_end):
num_of_batch = state.shape[
0] #188行s_replay = np.ndarray(shape=(self.replay_size, self.hist_size, self.dim),
# dtype=np.float32) =32
s = Variable(state)
s_dash = Variable(state_dash)
q = self.q_func(s) # Get Q-value
# Generate Target Signals
tmp = self.q_func_target(s_dash) # Q(s',*)
if self.use_gpu >= 0:
tmp = list(map(np.max, tmp.data.get())) # max_a Q(s',a)
else:
tmp = list(map(np.max, tmp.data)) # max_a Q(s',a)
max_q_dash = np.asanyarray(tmp, dtype=np.float32)
if self.use_gpu >= 0:
target = np.asanyarray(q.data.get(), dtype=np.float32)
else:
# make new array
target = np.array(q.data, dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = reward[i] + self.gamma * max_q_dash[i]
else:
tmp_ = reward[i]
action_index = self.action_to_index(action[i])
target[i, action_index] = tmp_
# TD-error clipping
if self.use_gpu >= 0:
target = cuda.to_gpu(target)
td = Variable(target) - q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td / abs(td_tmp) * (abs(td.data) >
1)
zero_val = np.zeros((self.replay_size, self.num_of_actions),
dtype=np.float32)
if self.use_gpu >= 0:
zero_val = cuda.to_gpu(zero_val)
zero_val = Variable(zero_val)
loss = F.mean_squared_error(td_clip, zero_val)
return loss, q
def stock_experience(self, time, state, action, reward, state_dash,
episode_end_flag): #def agent_end
self.data_index = time % self.data_size #---------------------------------------------------data_index
if episode_end_flag is True:
self.d[0][self.data_index] = state
self.d[1][self.data_index] = action
self.d[2][self.data_index] = reward
#print d[2]
else:
self.d[0][self.data_index] = state
self.d[1][self.data_index] = action
self.d[2][self.data_index] = reward
self.d[3][self.data_index] = state_dash
self.d[4][self.data_index] = episode_end_flag
def experience_replay(self, time):
if self.initial_exploration < time:
# Pick up replay_size number of samples from the Data
if time < self.data_size: # during the first sweep of the History Data
replay_index = np.random.randint(0, time,
(self.replay_size, 1))
else:
replay_index = np.random.randint(0, self.data_size,
(self.replay_size, 1))
s_replay = np.ndarray(shape=(self.replay_size, self.hist_size,
self.dim),
dtype=np.float32)
a_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.uint8)
r_replay = np.ndarray(shape=(self.replay_size, 1),
dtype=np.float32)
s_dash_replay = np.ndarray(shape=(self.replay_size, self.hist_size,
self.dim),
dtype=np.float32)
episode_end_replay = np.ndarray(shape=(self.replay_size, 1),
dtype=np.bool)
for i in xrange(self.replay_size):
s_replay[i] = np.asarray(self.d[0][replay_index[i]],
dtype=np.float32)
a_replay[i] = self.d[1][replay_index[i]]
r_replay[i] = self.d[2][replay_index[i]]
# if not (r_replay[i] == 1.):#--------------
# r_replay[i] = -3.#-----------------
s_dash_replay[i] = np.array(self.d[3][replay_index[i]],
dtype=np.float32)
episode_end_replay[i] = self.d[4][replay_index[i]]
if self.use_gpu >= 0:
s_replay = cuda.to_gpu(s_replay)
s_dash_replay = cuda.to_gpu(s_dash_replay)
# Gradient-based update
self.optimizer.zero_grads()
loss, _ = self.forward(
s_replay, a_replay, r_replay, s_dash_replay, episode_end_replay
) # def forward(self, state, action, reward, state_dash, episode_end):
loss.backward()
self.optimizer.update()
def q_func(self, state):
h4 = F.relu(self.model.l4(state / 255.0)) #---------- F.leaky_relu
#dp4 = F.dropout(h4, ratio=0.4, train=True)#----------ratio=0.3--0.4
q = self.model.q_value(h4)
return q
def q_func_target(self, state):
h4 = F.relu(self.model_target.l4(state / 255.0)) #--------F.leaky_
#dp4 = F.dropout(h4, ratio=0.3, train=True)#------------- dp4 = F.dropout(h4, ratio=0.3, train=True)
q = self.model_target.q_value(h4) #dp4
return q
def e_greedy(self, state, epsilon): #agent.start().eps =1.0
s = Variable(state)
q = self.q_func(s)
q = q.data
#print q.data.size#-----------------------------------------------------------------------------
if np.random.rand() < epsilon:
index_action = np.random.randint(0, self.num_of_actions)
print(" Random"), #￥---------------------2 " Random"
else:
if self.use_gpu >= 0:
index_action = np.argmax(q.get())
else:
index_action = np.argmax(q)
print("#Greedy"),
return self.index_to_action(index_action), q
def target_model_update(self):
self.model_target = copy.deepcopy(self.model)
def index_to_action(self, index_of_action):
return self.enable_controller[
index_of_action] #[index_of_action]=np.argmax(q)
def action_to_index(self, action):
return self.enable_controller.index(action)
class DN_class:
# Hyper-Parameters
gamma = 0.99 # Discount factor
initial_exploration = 100#10**4 # Initial exploratoin. original: 5x10^4
replay_size = 32 # Replay (batch) size
target_model_update_freq = 10**4 # Target update frequancy. original: 10^4
data_size = 10**5 # Data size of history. original: 10^6
def __init__(self, enable_controller=[0, 1, 3, 4]):
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller # Default setting : "Breakout"
print "Initializing DN..."
# Initialization of Chainer 1.1.0 or older.
# print "CUDA init"
# cuda.init()
print "Model Building"
self.model = FunctionSet(
l1=F.Convolution2D(4, 32, ksize=8, stride=4, nobias=False, wscale=np.sqrt(2)),
l2=F.Convolution2D(32, 64, ksize=4, stride=2, nobias=False, wscale=np.sqrt(2)),
l3=F.Convolution2D(64, 64, ksize=3, stride=1, nobias=False, wscale=np.sqrt(2)),
l4=F.Linear(3136, 256, wscale=np.sqrt(2)),
l5=F.Linear(3136, 256, wscale=np.sqrt(2)),
l6=F.Linear(256, 1, initialW=np.zeros((1, 256), dtype=np.float32)),
l7=F.Linear(256, self.num_of_actions, initialW=np.zeros((self.num_of_actions, 256),
dtype=np.float32)),
q_value=DN_out.DN_out(1, self.num_of_actions, self.num_of_actions, nobias = True)
).to_gpu()
if args.resumemodel:
# load saved model
serializers.load_npz(args.resumemodel, self.model)
print "load model from resume.model"
self.model_target = copy.deepcopy(self.model)
print "Initizlizing Optimizer"
self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.0001)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
if args.resumeD1 and args.resumeD2:
# load saved D1 and D2
npz_tmp1 = np.load(args.resumeD1)
print "finished loading half of D data"
npz_tmp2 = np.load(args.resumeD2)
self.D = [npz_tmp1['D0'],
npz_tmp1['D1'],
npz_tmp1['D2'],
npz_tmp2['D3'],
npz_tmp2['D4']]
npz_tmp1.close()
npz_tmp2.close()
print "loaded stored all D data"
else:
self.D = [np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, 4, 84, 84), dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)]
print "initialized D data"
def forward(self, state, action, Reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
Q = self.Q_func(s) # Get Q-value
# Generate Target Signals
tmp2 = self.Q_func(s_dash)
tmp2 = list(map(np.argmax, tmp2.data.get())) # argmaxQ(s',a)
tmp = self.Q_func_target(s_dash) # Q'(s',*)
tmp = list(tmp.data.get())
# select Q'(s',*) due to argmaxQ(s',a)
res1 = []
for i in range(num_of_batch):
res1.append(tmp[i][tmp2[i]])
#max_Q_dash = np.asanyarray(tmp, dtype=np.float32)
max_Q_dash = np.asanyarray(res1, dtype=np.float32)
target = np.asanyarray(Q.data.get(), dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = np.sign(Reward[i]) + self.gamma * max_Q_dash[i]
else:
tmp_ = np.sign(Reward[i])
action_index = self.action_to_index(action[i])
target[i, action_index] = tmp_
# TD-error clipping
td = Variable(cuda.to_gpu(target)) - Q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td/abs(td_tmp) * (abs(td.data) > 1)
zero_val = Variable(cuda.to_gpu(np.zeros((self.replay_size, self.num_of_actions), dtype=np.float32)))
loss = F.mean_squared_error(td_clip, zero_val)
return loss, Q
def stockExperience(self, time,
state, action, reward, state_dash,
episode_end_flag):
data_index = time % self.data_size
if episode_end_flag is True:
self.D[0][data_index] = state
self.D[1][data_index] = action
self.D[2][data_index] = reward
else:
self.D[0][data_index] = state
self.D[1][data_index] = action
self.D[2][data_index] = reward
self.D[3][data_index] = state_dash
self.D[4][data_index] = episode_end_flag
def experienceReplay(self, time):
if self.initial_exploration < time:
# Pick up replay_size number of samples from the Data
if time < self.data_size: # during the first sweep of the History Data
replay_index = np.random.randint(0, time, (self.replay_size, 1))
else:
replay_index = np.random.randint(0, self.data_size, (self.replay_size, 1))
s_replay = np.ndarray(shape=(self.replay_size, 4, 84, 84), dtype=np.float32)
a_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.uint8)
r_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.float32)
s_dash_replay = np.ndarray(shape=(self.replay_size, 4, 84, 84), dtype=np.float32)
episode_end_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.bool)
for i in xrange(self.replay_size):
s_replay[i] = np.asarray(self.D[0][replay_index[i]], dtype=np.float32)
a_replay[i] = self.D[1][replay_index[i]]
r_replay[i] = self.D[2][replay_index[i]]
s_dash_replay[i] = np.array(self.D[3][replay_index[i]], dtype=np.float32)
episode_end_replay[i] = self.D[4][replay_index[i]]
s_replay = cuda.to_gpu(s_replay)
s_dash_replay = cuda.to_gpu(s_dash_replay)
# Gradient-based update
self.optimizer.zero_grads()
loss, _ = self.forward(s_replay, a_replay, r_replay, s_dash_replay, episode_end_replay)
loss.backward()
self.optimizer.update()
def Q_func(self, state):
print 'now Q_func is implemented'
h1 = F.relu(self.model.l1(state / 254.0)) # scale inputs in [0.0 1.0]
h2 = F.relu(self.model.l2(h1))
h3 = F.relu(self.model.l3(h2))
h4 = F.relu(self.model.l4(h3)) # left side connected with s value
h5 = F.relu(self.model.l5(h3)) # right side connected with A value
h6 = self.model.l6(h4) # s value
h7 = self.model.l7(h5) # A value
Q = self.model.q_value(h6, h7) # Q value
return Q
def Q_func_target(self, state):
print 'now Q_func_target is implemented'
h1 = F.relu(self.model_target.l1(state / 254.0)) # scale inputs in [0.0 1.0]
h2 = F.relu(self.model_target.l2(h1))
h3 = F.relu(self.model_target.l3(h2))
h4 = F.relu(self.model_target.l4(h3)) # left side connected with s value
h5 = F.relu(self.model_target.l5(h3)) # right side connected with A value
h6 = self.model_target.l6(h4) # s value
h7 = self.model_target.l7(h5) # A value
Q = self.model_target.q_value(h6, h7) # Q value
return Q
def e_greedy(self, state, epsilon):
s = Variable(state)
Q = self.Q_func(s)
Q = Q.data
if np.random.rand() < epsilon:
index_action = np.random.randint(0, self.num_of_actions)
print "RANDOM"
else:
index_action = np.argmax(Q.get())
print "GREEDY"
return self.index_to_action(index_action), Q
def target_model_update(self):
self.model_target = copy.deepcopy(self.model)
def index_to_action(self, index_of_action):
return self.enable_controller[index_of_action]
def action_to_index(self, action):
return self.enable_controller.index(action)
class QNet:
# Hyper-Parameters
gamma = 0.99 # 報酬の割引率
initial_exploration = 10**3 # Initial exploratoin. original: 5x10^4
replay_size = 32 # Replay (batch) size
target_model_update_freq = 10**4 # Target update frequancy. original: 10^4
data_size = 10**5 # Data size of history. original: 10^6
hist_size = 1 #original: 4
save_model_freq = 10**4 # モデルを保存する頻度
def __init__(self, use_gpu, enable_controller, dim):
self.use_gpu = use_gpu
self.num_of_actions = len(enable_controller)
self.enable_controller = enable_controller
self.dim = dim
print("Initializing Q-Network...")
print("Input Dim of Q-Network : ",self.dim*self.hist_size)
hidden_dim = 256
self.model = FunctionSet(
l4=F.Linear(self.dim*self.hist_size, hidden_dim,
wscale=np.sqrt(2)),
q_value=F.Linear(hidden_dim, self.num_of_actions,
initialW=np.zeros((self.num_of_actions, hidden_dim),
dtype=np.float32))
)
if self.use_gpu >= 0:
self.model.to_gpu()
self.model_target = copy.deepcopy(self.model)
self.optimizer = optimizers.RMSpropGraves(lr=0.00025, alpha=0.95, momentum=0.95, eps=0.0001)
self.optimizer.setup(self.model.collect_parameters())
# History Data : D=[s, a, r, s_dash, end_episode_flag]
self.d = [np.zeros((self.data_size, self.hist_size, self.dim),
dtype=np.uint8),
np.zeros(self.data_size, dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.int8),
np.zeros((self.data_size, self.hist_size, self.dim),
dtype=np.uint8),
np.zeros((self.data_size, 1), dtype=np.bool)]
def forward(self, state, action, reward, state_dash, episode_end):
num_of_batch = state.shape[0]
s = Variable(state)
s_dash = Variable(state_dash)
q = self.q_func(s) # Get Q-value
# Generate Target Signals
tmp = self.q_func_target(s_dash) # Q(s',*)
if self.use_gpu >= 0:
tmp = list(map(np.max, tmp.data.get())) # max_a Q(s',a)
else:
tmp = list(map(np.max, tmp.data)) # max_a Q(s',a)
max_q_dash = np.asanyarray(tmp, dtype=np.float32)
if self.use_gpu >= 0:
target = np.asanyarray(q.data.get(), dtype=np.float32)
else:
# make new array
target = np.array(q.data, dtype=np.float32)
for i in xrange(num_of_batch):
if not episode_end[i][0]:
tmp_ = reward[i] + self.gamma * max_q_dash[i]
else:
tmp_ = reward[i]
action_index = self.action_to_index(action[i])
target[i, action_index] = tmp_
# TD-error clipping
if self.use_gpu >= 0:
target = cuda.to_gpu(target)
td = Variable(target) - q # TD error
td_tmp = td.data + 1000.0 * (abs(td.data) <= 1) # Avoid zero division
td_clip = td * (abs(td.data) <= 1) + td/abs(td_tmp) * (abs(td.data) > 1)
zero_val = np.zeros((self.replay_size, self.num_of_actions), dtype=np.float32)
if self.use_gpu >= 0:
zero_val = cuda.to_gpu(zero_val)
zero_val = Variable(zero_val)
loss = F.mean_squared_error(td_clip, zero_val)
return loss, q
def stock_experience(self, time,state, action, reward,
state_dash,episode_end_flag):
data_index = time % self.data_size #timeを引数に入れることでqueueを実現
if episode_end_flag is True: # ep_endがTrueならstate_dashが全て0になる
self.d[0][data_index] = state
self.d[1][data_index] = action
self.d[2][data_index] = reward
else:
self.d[0][data_index] = state
self.d[1][data_index] = action
self.d[2][data_index] = reward
self.d[3][data_index] = state_dash
self.d[4][data_index] = episode_end_flag
def experience_replay(self, time):
if self.initial_exploration < time:
if time < self.data_size: #during the first sweep of the History
replay_index = np.random.randint(0, time, (self.replay_size, 1))
else:
replay_index = np.random.randint(0, self.data_size, (self.replay_size, 1))
s_replay = np.ndarray(shape=(self.replay_size, self.hist_size, self.dim), dtype=np.float32)
a_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.uint8)
r_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.float32)
s_dash_replay = np.ndarray(shape=(self.replay_size, self.hist_size, self.dim), dtype=np.float32)
episode_end_replay = np.ndarray(shape=(self.replay_size, 1), dtype=np.bool)
for i in xrange(self.replay_size):
s_replay[i] = np.asarray(self.d[0][replay_index[i]], dtype=np.float32)
a_replay[i] = self.d[1][replay_index[i]]
r_replay[i] = self.d[2][replay_index[i]]
s_dash_replay[i] = np.array(self.d[3][replay_index[i]], dtype=np.float32)
episode_end_replay[i] = self.d[4][replay_index[i]]
if self.use_gpu >= 0:
s_replay = cuda.to_gpu(s_replay)
s_dash_replay = cuda.to_gpu(s_dash_replay)
# Gradient-based update
self.optimizer.zero_grads()
loss, _ = self.forward(s_replay, a_replay, r_replay, s_dash_replay, episode_end_replay)
loss.backward()
self.optimizer.update()
def q_func(self, state):
h4 = F.relu(self.model.l4(state / 255.0))
q = self.model.q_value(h4)
return q
def q_func_target(self, state):
h4 = F.relu(self.model_target.l4(state / 255.0))
q = self.model_target.q_value(h4)
return q
def e_greedy(self, state, epsilon):
s = Variable(state)
q = self.q_func(s)
q = q.data
if np.random.rand() < epsilon:
index_action = np.random.randint(0, self.num_of_actions)
print(" Random"),
else:
if self.use_gpu >= 0:
index_action = np.argmax(q.get())
else:
index_action = np.argmax(q)
print("#Greedy"),
return self.index_to_action(index_action), q
def target_model_update(self):
self.model_target = copy.deepcopy(self.model)
def index_to_action(self, index_of_action):
return self.enable_controller[index_of_action]
def action_to_index(self, action):
return self.enable_controller.index(action)
def save_model(self,folder,time):
try:
model_path = "./%s/%dmodel"%(folder,time)
serializers.save_npz(model_path,self.model)
except:
import traceback
import sys
traceback.print_exc()
sys.exit()
print "model is saved!!(Model_Path=%s)"%(model_path)
print "----------------------------------------------"
def load_model(self,folder,model_num):
try:
model_path = "./%s/%dmodel"%(folder,model_num)
serializers.load_npz(model_path,self.model)
except:
import traceback
import sys
traceback.print_exc()
sys.exit()
print "model load is done!!(Model_Path=%s)"%(model_path)
print "----------------------------------------------"
self.target_model_update()