(进阶)004 - AWS DeepRacer重要资料合集

Deppracer 进阶必看的资料合集

1. reward_function 入口参数说明

https://docs.aws.amazon.com/deepracer/latest/developerguide/deepracer-reward-function-input.html
1

{
    "all_wheels_on_track": Boolean,        # flag to indicate if the agent is on the track
    "x": float,                            # agent's x-coordinate in meters
    "y": float,                            # agent's y-coordinate in meters
    "closest_objects": [int, int],         # zero-based indices of the two closest objects to the agent's current position of (x, y).
    "closest_waypoints": [int, int],       # indices of the two nearest waypoints.
    "distance_from_center": float,         # distance in meters from the track center 
    "is_crashed": Boolean,                 # Boolean flag to indicate whether the agent has crashed.
    "is_left_of_center": Boolean,          # Flag to indicate if the agent is on the left side to the track center or not. 
    "is_offtrack": Boolean,                # Boolean flag to indicate whether the agent has gone off track.
    "is_reversed": Boolean,                # flag to indicate if the agent is driving clockwise (True) or counter clockwise (False).
    "heading": float,                      # agent's yaw in degrees
    "objects_distance": [float, ],         # list of the objects' distances in meters between 0 and track_length in relation to the starting line.
    "objects_heading": [float, ],          # list of the objects' headings in degrees between -180 and 180.
    "objects_left_of_center": [Boolean, ], # list of Boolean flags indicating whether elements' objects are left of the center (True) or not (False).
    "objects_location": [(float, float),], # list of object locations [(x,y), ...].
    "objects_speed": [float, ],            # list of the objects' speeds in meters per second.
    "progress": float,                     # percentage of track completed
    "speed": float,                        # agent's speed in meters per second (m/s)
    "steering_angle": float,               # agent's steering angle in degrees
    "steps": int,                          # number steps completed
    "track_length": float,                 # track length in meters.
    "track_width": float,                  # width of the track
    "waypoints": [(float, float), ]        # list of (x,y) as milestones along the track center

}
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

重要参数有：

1. all_wheels_on_track
2. x,y
3. distance_from_center
4. heading
5. progress
6. speed
7. steps
8. track_width
9. waypoints

2. 常见 reward_function 实例

1. 走中线

def reward_function(params):
    # Read input parameters
    track_width = params['track_width']
    distance_from_center = params['distance_from_center']

    # Calculate 3 markers that are increasingly further away from the center line
    marker_1 = 0.1 * track_width
    marker_2 = 0.25 * track_width
    marker_3 = 0.5 * track_width

    # Give higher reward if the car is closer to center line and vice versa
    if distance_from_center <= marker_1:
        reward = 1
    elif distance_from_center <= marker_2:
        reward = 0.5
    elif distance_from_center <= marker_3:
        reward = 0.1
    else:
        reward = 1e-3  # likely crashed/ close to off track

    return reward
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

2. 保持在界内

def reward_function(params):
    # Read input parameters
    all_wheels_on_track = params['all_wheels_on_track']
    distance_from_center = params['distance_from_center']
    track_width = params['track_width']
    
    # Give a very low reward by default
    reward = 1e-3

    # Give a high reward if no wheels go off the track and 
    # the car is somewhere in between the track borders 
    if all_wheels_on_track and (0.5*track_width - distance_from_center) >= 0.05:
        reward = 1.0

    # Always return a float value
    return reward
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

3. 防止蛇形

def reward_function(params):
    # Read input parameters
    all_wheels_on_track = params['all_wheels_on_track']
    distance_from_center = params['distance_from_center']
    track_width = params['track_width']
    
    # Give a very low reward by default
    reward = 1e-3

    # Give a high reward if no wheels go off the track and 
    # the car is somewhere in between the track borders 
    if all_wheels_on_track and (0.5*track_width - distance_from_center) >= 0.05:
        reward = 1.0
    # Always return a float value
    return reward
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

3. log 分析

link: lulu2002

使用方法:

1. 下载你的log(Training log 或者 Evaluation log)
2. 点击按钮上传模型
3. 在表格点击你要分析的episod
4. 右边图像中可以看到本次的小车路径以及速度分布
5. 其他一些信息也可以通过面板找到

3. 通过AWS deepracer 社区获取赛道 waypoint

link: aws-deepracer-community

在这里你可以得到所有赛道的waypoint 数据，以及赛道总长度和赛道宽度，以及发布时间，
这里的2022_may_pro.npy 里面 存放了该赛道 waypoint 值。

4. 获取AWS deepracer 社区信息

link: Deeprace社区

在Deepracer 社区你可以获取 你需要的信息，其中 deepracer-for-cloud 是一个可以部署到云上或本地的一个库，因为在AWS上直接training是需要花钱的 3.55$ / h，也就是近 24￥/ h ，平民玩家伤不起，如果你对这个有兴趣可以在本地部署一个，不过这个对电脑要求也挺高的，我的电脑 16G内存，3G GPU，勉强能带动，只能通过较小的batch size来训练，否则CPU/GPU 使用过高容易被系统kill掉。

CPU 基本上80% 以上

GPU 基本 95% 以上

时不时就挂了，内存溢出

建议32G 内存 + 8G GPU

5. 计算最佳路线

link: cdthompson/deepracer-k1999-race-lines

最佳线路的原理是: 将中间点的曲率用相邻两点来代替，使得曲线更加光滑平整，后面的博客中将提取他的核心代码进行计算。

6. Capstone

link: Capstone

1. 计算动作空间以及数据分析和可视化
2. 计算最佳速度以及速度分布和可视化 RaceLine_Speed_ActionSpace
3. 计算最佳路线， 这里借鉴了K1999的算法 Race-Line-Calculation
4. 使用selenium 实现自动提交和自动提交训练 Selenium_DeepRacer

7. 参考

1. https://github.com/cdthompson/deepracer-k1999-race-lines
2. https://github.com/aws-deepracer-community/deepracer-analysis
3. https://github.com/aws-deepracer-community/deepracer-simapp/tree/master/bundle/deepracer_simulation_environment/share/deepracer_simulation_environment/routes

以上内容我将会在后面的博客通过自己的理解一一讲解用法，和使用技巧。

>>> 如果你觉得我的文章对你有用，不妨 【点赞】 加 【关注】，你的支持是我持续写作的动力，thank you! <<<