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
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
s += [get_s('0')]
while abs((fc.get_time() - start_time - normal_working) *
fc.get_velocity('0') / 100000) < 0.3 and abs(
normal_working * fc.get_velocity('0') / 100000) < 60:
p = ddpg.predict(s[-1])
a += [p[0]]
if ddpg.frame < ddpg.REPLAY_CAPACITY / 8:
a[-1] = np.array([0., 100.])
a[-1] = np.random.normal(
a[-1] * np.random.uniform(max(1 - np.sqrt(var[0]), 0), 2), var, 2)
fc.ctrl('0', *(a[-1]))
run_time = fc.get_time()
while fc.get_time() - run_time < FRAME_SPACING:
if ddpg.frame > ddpg.REPLAY_CAPACITY / 8:
ddpg.fit()
var *= 0.9999
else:
fc.set_time_speed(0)
ddpg = DDPG.DDPG(11,2,np.array([np.pi/3, 256]))
var=np.array([np.pi/3,30])
times = 1
fc.start()
while times:
start_time = fc.get_time()
normal_working = 0
s,a,r,s_ = [],[],[],[]
s += [get_s('0')]
while abs((fc.get_time()-start_time-normal_working)*fc.get_velocity('0')/100000) < 0.3 and abs(normal_working*fc.get_velocity('0')/100000) < 60:
p=ddpg.predict(s[-1])
a += [p[0][0]]
a[-1] += np.random.normal(a[-1]*np.random.uniform(max(1-np.sqrt(var[0]),0),2), var,2)
fc.ctrl('0',*(a[-1]+np.array([0,192])))
run_time = fc.get_time()
while fc.get_time()-run_time < FRAME_SPACING:
if ddpg.frame>ddpg.REPLAY_CAPACITY/8:
ddpg.fit()
var*=0.9999
else:
fc.sleep(1)
r += [get_r('0')]
s_ += [get_s('0')]
else:
fc.sleep(1)
if ddpg.frame > 500:
var *= .999
print('var', var)
r += [get_r('0') - np.square(*a[-1] * 2)]
s_ += [get_s('0')]
if r[-1] <= -1000:
break_count += 1
else:
break_count = 0
ddpg.record(s[pointer], a[pointer], r[-1] / 100, s_[-1])
pointer += 1
s += [s_[-1]]
print('a', *a[-1], '\t', 'r', r[-1], '\t', 'frame', ddpg.frame)
print('v', fc.get_velocity('0'), '\n')
fc.set_car('0', (430 + np.random.normal(0, 5), 300), np.pi / 2, 0.)
times += 1
if times % 20 == 0:
fc.map_clear()
# fit_time = time.time()
# while time.time()-fit_time < 5:
# ddpg.fit()
degree = 0
velocity = 100
times = 1
fc.set_time_speed(1)
fc.start()
reset_time = fc.get_time()
while times:
sight = cv2.resize(np.array(fc.get_car_sight("0")), (16, 16))
sight = np.reshape(sight, [16, 16, 1])
out = model.predict({
input_map: [sight],
input_degree: [np.array([degree])],
input_velocity: [np.array([fc.get_velocity('0')])]
})
degree = out[0][0] * np.pi / 3
# velocity=(out[0][1]+1)*200
velocity = 100
fc.ctrl('0', degree, velocity)
print(out)
print(len(train_y))
train_sight += [sight]
train_degree += [np.array([degree])]
train_velocity += [np.array([velocity])]
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)])
train_left += [left]
train_right += [right]
train_state += [
np.array([degree / np.pi / 2 * 3 + 0.5,
fc.get_velocity('0') / 200])
]
out = model_ctrl.predict({
input_left: [train_left[-1]],
input_right: [train_right[-1]],
input_state: [train_state[-1]]
})
if not times % 20:
mid = ti['mid']
out[0][0] = (mid[0][0] -
(wight - 1) / 2) * 0.04 + (mid[len(mid) // 3][0] -
(wight - 1) / 2) * 0.04
out[0][0] = out[0][0] / 2. / np.pi * 3. + 0.5
out[0][1] = 300 - fc.get_velocity('0') * 2