def get_s(name):
sight = fc.get_car_sight(name)
ti = fc.get_ti(name)
mid = ti['mid']
while len(mid) < 5:
mid.append(mid[len(mid) - 1])
mid = np.array([mid[i * (len(mid) - 1) // 5] for i in range(5)])
mid = mid - np.array([len(fc.get_car_sight(name)[0]) / 2, 0])
s = np.reshape(mid, 10)
# right = ti['right']
# left = ti['left']
# if len(left) == 0:
# left.append([0,0])
# if len(right) == 0:
# right.append([wight-1,hight-1])
# while len(right)<5:
# right.append(right[len(right)-1])
# while len(left)<5:
# left.append(left[len(left)-1])
# right = np.array([right[i*(len(right)-1)//5] for i in range(5)])
# left = np.array([left[i*(len(left)-1)//5] for i in range(5)])
# s = np.concatenate([np.reshape(right,10), np.reshape(left,10)], 0)
return s
def get_r(name):
r, ti = 0, fc.get_ti(name)
mid = ti['mid']
sight = fc.get_car_sight(name)
if sight[mid[0][1]][mid[0][0]] < 0.5:
r = -1000
else:
r = -np.square(mid[0][0] - len(fc.get_car_sight(name)[0]) / 2)
#r -= np.square((100-fc.get_velocity(name))/50)
return r
def get_r(name):
sight = fc.get_car_sight(name)
wight = len(sight[0])
hight = len(sight)
r, ti = 0, fc.get_ti(name)
mid = ti['mid']
if sight[mid[0][1]][mid[0][0]] < 0.5:
r = -35
else:
r = -np.abs(mid[0][0] - wight / 2)
return r
def get_s(name):
sight = fc.get_car_sight(name)
wight = len(sight[0])
hight = len(sight)
ti = fc.get_ti(name)
mid = ti['mid']
while len(mid) < 5:
mid.append(mid[len(mid) - 1])
mid = np.array([mid[i * (len(mid) - 1) // 6] for i in range(5)])
mid = mid - np.array([wight / 2, 0])
mid = np.array([0, hight]) - mid
s = np.reshape(mid, 10)
s = np.concatenate([s, np.array([fc.get_velocity(name)])], 0)
return s
mid = ti['mid']
if sight[mid[0][1]][mid[0][0]] < 0.5:
r = -35
else:
r = -np.abs(mid[0][0] - wight / 2)
return r
FRAME_SPACING = 20
fc.game_init('FCMAP0.PNG')
fc.add_car('0', (90, 255))
fc.set_car('0', (90, 255), np.pi / 2, 0)
#fc.show_car_sight('0')
wight = len(fc.get_car_sight('0')[0])
fc.set_time_speed(0)
ddpg = MDDPG.DDPG(11, 2, 2, np.array([np.pi / 3, 256]))
var = np.array([np.pi / 3, 100])
times = 1
fc.start()
while times:
start_time = fc.get_time()
normal_working = 0
s, a, r, s_ = [], [], [], []
if times % 10 == 1:
var += np.array([np.pi / 3, 100]) / 5
value_model = tfl.regression(value,
placeholder=y,
optimizer='adam',
loss=value_loss,
trainable_vars=value_vars,
batch_size=64,
name='target_value',
op_name='value_model')
model = tfl.DNN(tf.concat([ctrl, value], 1))
fc.game_init("FCMAP0.PNG")
fc.add_car("0", (430, 240))
fc.set_car("0", (430, 240), np.pi / 2, 0)
fc.show_car_sight("0")
wight = len(fc.get_car_sight("0")[0])
train_sight = []
train_degree = []
train_velocity = []
train_y = []
degree = 0
velocity = 100
times = 1
fc.set_time_speed(1)
fc.start()
reset_time = fc.get_time()
while times:
placeholder=None,
optimizer='adam',
loss=ctrl_loss,
trainable_vars=ctrl_vars,
batch_size=512,
name='target_ctrl',
op_name='ctrl_reg')
#model_value_example=tfl.DNN(value_example)
model_ctrl = tfl.DNN(tf.concat([ctrl, value], 1))
fc.game_init("FCMAP0.PNG")
fc.add_car("0", (430, 240))
fc.set_car("0", (435, 240), np.pi / 2, 0)
fc.show_car_sight("0")
wight = len(fc.get_car_sight("0")[0])
hight = len(fc.get_car_sight("0"))
train_left = []
train_right = []
train_state = []
train_example_ctrl = []
train_y = []
degree = 0
velocity = 100
times = 0
fc.set_time_speed(1)
fc.start()
reset_time = fc.get_time()