本文為英文版的機器翻譯版本,如內容有任何歧義或不一致之處,概以英文版為準。
AWS DeepRacer 獎勵函數範例
以下列出 AWS DeepRacer 獎勵功能的一些範例。
範例 1:遵循時間試驗中的中心線
此範例會判斷代理程式與中心線距離多遠,並在代理程式與賽道中心較接近時給予較高的獎勵,鼓勵代理程式緊跟著中心線。
def reward_function(params):
'''
Example of rewarding the agent to follow center line
'''
# 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
示例 2:在時間試驗中留在兩個邊界內
這個例子簡單地給出了很高的回報,如果代理停留在邊界內,並讓代理找出完成一圈的最佳路徑。編程和理解很容易,但可能需要更長的時間才能收斂。
def reward_function(params):
'''
Example of rewarding the agent to stay inside the two borders of the track
'''
# 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
示例 3:在時間試驗中防止鋸齒形
此範例會鼓勵代理程式依循中心線,但會在其轉向過大時以較低的獎勵進行懲罰,有助於防止蛇行行為。代理程式學會在模擬器中順利駕駛,並且在部署到實體車輛時可能會保持相同的行為。
def reward_function(params):
'''
Example of penalize steering, which helps mitigate zig-zag behaviors
'''
# Read input parameters
distance_from_center = params['distance_from_center']
track_width = params['track_width']
abs_steering = abs(params['steering_angle']) # Only need the absolute steering angle
# Calculate 3 marks that are farther and father 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 = 1.0
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
# Steering penality threshold, change the number based on your action space setting
ABS_STEERING_THRESHOLD = 15
# Penalize reward if the car is steering too much
if abs_steering > ABS_STEERING_THRESHOLD:
reward *= 0.8
return float(reward)
例子 4:停留在一條車道上,而不會撞到固定的障礙物或移動車輛
此獎勵功能獎勵留在軌道邊界內的代理商,並懲罰代理人因太靠近它前面的物體而受到懲罰。代理程式可以變換車道來避免衝撞。總獎勵是獎勵和懲罰的加權總和。這個例子給了更多的權重,以避免崩潰的懲罰。試驗不同的平均權重,以針對不同的行為結果進行訓練。
import math
def reward_function(params):
'''
Example of rewarding the agent to stay inside two borders
and penalizing getting too close to the objects in front
'''
all_wheels_on_track = params['all_wheels_on_track']
distance_from_center = params['distance_from_center']
track_width = params['track_width']
objects_location = params['objects_location']
agent_x = params['x']
agent_y = params['y']
_, next_object_index = params['closest_objects']
objects_left_of_center = params['objects_left_of_center']
is_left_of_center = params['is_left_of_center']
# Initialize reward with a small number but not zero
# because zero means off-track or crashed
reward = 1e-3
# Reward if the agent stays inside the two borders of the track
if all_wheels_on_track and (0.5 * track_width - distance_from_center) >= 0.05:
reward_lane = 1.0
else:
reward_lane = 1e-3
# Penalize if the agent is too close to the next object
reward_avoid = 1.0
# Distance to the next object
next_object_loc = objects_location[next_object_index]
distance_closest_object = math.sqrt((agent_x - next_object_loc[0])**2 + (agent_y - next_object_loc[1])**2)
# Decide if the agent and the next object is on the same lane
is_same_lane = objects_left_of_center[next_object_index] == is_left_of_center
if is_same_lane:
if 0.5 <= distance_closest_object < 0.8:
reward_avoid *= 0.5
elif 0.3 <= distance_closest_object < 0.5:
reward_avoid *= 0.2
elif distance_closest_object < 0.3:
reward_avoid = 1e-3 # Likely crashed
# Calculate reward by putting different weights on
# the two aspects above
reward += 1.0 * reward_lane + 4.0 * reward_avoid
return reward