Esempio n. 1
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    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])
Esempio n. 2
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    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
Esempio n. 5
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def drive_v2():
    diff = getXdiff()
    speed = getSpeed()
    points = getPoints()
    edge = getPlayerEdge()

    car_shifted = func.get_shifted(
        func.get_player_detailed_pos(edge[0], getPlayerVertex()))
    way_width = points[3][0] - points[2][0]

    o1, o2 = getOrigin(), getOrigin2()
    print(func.get_reverse_gradient(o1, o2))

    # diff가 음수일땐 차가 좌측에 있을 때, diff가 양수일땐 차가 우측에 있을 때
    # diff가 음수면서, car_shifted가 양수면 (차가 좌측으로 휘어있으면), 우측으로 틀어야 함
    # diff가 양수면서, car_shifted가 음수면 (차가 우측으로 휘어있으면), 좌측으로 틀어야 함

    if speed < 100:
        speed_time_val = 1
    else:
        speed_time_val = 100 / speed
Esempio n. 6
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    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