def observation(self):
# Image Processing에서 값을 받아옴
way_middle_pos = ip.getOrigin()
way_dot_pos = ip.getPoints()
player_pos = ip.getPlayerVertex()
# Env1에서 값을 가져옴
global Env1_action
# 값을 가공함
speed = min(ip.getSpeed() / 250, 1)
curved = min((way_dot_pos[0][0] - way_dot_pos[2][0]) +
(way_dot_pos[1][0] - way_dot_pos[3][0]), 1)
middle_diff = min(abs(way_middle_pos[0] - player_pos[0]) / 80, 1)
reverse = 1 if ip.getReverse() else 0
e1_act = Env1_action / 2
# 추가로 저장해야 할 값 설정
self.way_width = abs(way_dot_pos[2][0] - way_dot_pos[3][0])
self.way_player_diff = max(abs(way_dot_pos[2][0] - player_pos[0]),
abs(way_dot_pos[3][0] - player_pos[0]))
# 그대로 return
return np.array([speed, curved, middle_diff, reverse, e1_act])
def step(self, action):
# environment에 action을 취하는 것이며
# 다음 관찰, 보상, 에피소드가 종료되었는지, 기타 정보 4개를 return한다.
start_step = time.time()
self.pre_direction = change_direction(self.pre_direction, action)
road_center = ip.getOrigin()
roadtop_center = ip.getOrigin2()
road_points = ip.getPoints()
player_pos = ip.getPlayerVertex()
road_diff = self.get_road_diff(road_points)
reverse = ip.getReverse()
cur_speed = ip.getSpeed()
car_edge = ip.getPlayerEdge()
car_shifted = func.get_shifted(
func.get_player_detailed_pos(car_edge[0], player_pos))
car_shifted = int(max(0, min(car_shifted * 100 + 100, 200)))
road_shifted = func.get_reverse_gradient(road_center, roadtop_center)
road_shifted = int(max(0, min(road_shifted * 100 + 100, 200)))
self.speed_queue.popleft()
self.speed_queue.append(cur_speed)
# observation은 총 3개 - [ 중앙의 정도, 속도, 길의 커브정도] 로 오고
# 보상으로 중앙의 정도에 대한 보상(reward_diff), 속도에 대한 보상(reward_speed), 거꾸로 갈 때 음수를 주는 보상(reward_backward)이 온다.
reward_diff, diff = self.reward_player_reddot_diff(
road_center, player_pos, road_points, road_diff)
reward_speed_diff = self.reward_speed_diff()
reward_backward = self.reward_going_back(reverse)
observation = np.array([
diff / 200, cur_speed / 200, road_diff / 200, car_shifted / 200,
road_shifted / 200
])
if self.speed_queue[0] == 0 and self.speed_queue[1] == 0:
print("Episode Ended, with return state True")
return observation, -20, True, {}
if ip.isLap2() and not self.continuos: # 두 번째 맵에 도달하면 게임 종료 및 로그 남김
cur_time = datetime.datetime.now()
print("한바퀴 돌기 성공!")
with open("success_" + cur_time.strftime("%d%H%M%S") + ".txt",
"w",
encoding="utf8") as file:
file.writelines("한 바퀴 주행 성공!\n")
file.writelines(str(cur_time - self.time1))
return observation, 1000, True, {}
while True: # 시간 Delay줌
end_time = time.time()
if end_time - start_step > 0.025:
break
self.pre_speed = cur_speed
print(observation, reward_diff + reward_speed_diff + reward_backward,
False, {'direction': printLoc[action]})
return observation, reward_diff + reward_speed_diff + reward_backward, False, {
'direction': printLoc[action]
}
def observation(self):
minimap = ip.getSimpleMap() / 255
# print(minimap.shape)
# Image Processing에서 값을 받아옴
finished = ip.isLap2() # 추후에 추가될 변수, 맵을 완주함을 표시함, bool형태로 받으면 좋을듯
# 값을 가공함
speed = ip.getSpeed() # max값이 250이라 가정
reverse = ip.getReverse()
# 그대로 return
return minimap, speed, reverse, finished
def observation(self):
minimap = ip.getSimpleMap() / 255
# print(minimap.shape)
# Image Processing에서 값을 받아옴
way_middle_pos = ip.getOrigin()
way_dot_pos = ip.getPoints()
player_pos = ip.getPlayerVertex()
# 값을 가공함
speed = min(ip.getSpeed() / 250, 1)
middle_diff = min(abs(way_middle_pos[0] - player_pos[0]) / 80, 1)
reverse = ip.getReverse()
# 추가로 저장해야 할 값 설정
self.way_width = abs(way_dot_pos[2][0] - way_dot_pos[3][0])
self.way_player_diff = max(abs(way_dot_pos[2][0] - player_pos[0]), abs(way_dot_pos[3][0] - player_pos[0]))
# 그대로 return
return minimap, speed, middle_diff, reverse
def observation(self):
# 미니맵 정보를 완전히 받아오는 무언가의 함수
minimap = np.ravel(list(ip.getSimpleMap()), order='C')
# print(minimap.shape)
# print(minimap)
way_middle_pos = (ip.getOrigin())
way_dot_pos = ip.getPoints()
player_pos = ip.getPlayerVertex()
speed = np.array([ip.getSpeed()])
reverse = np.array([1 if ip.getReverse() else 0])
curved = np.array([(way_dot_pos[0][0] - way_dot_pos[2][0]) +
(way_dot_pos[1][0] - way_dot_pos[3][0])])
middle_diff = np.array([abs(way_middle_pos[0] - player_pos[0])])
way_down_length = abs(way_dot_pos[2][0] - way_dot_pos[3][0])
# print(speed, middle_diff, curved, reverse)
result = np.concatenate((minimap, speed, middle_diff, curved, reverse))
result = np.array(result, dtype=np.float32)
return result, speed, reverse, middle_diff, way_down_length