AWS Deepracer organised by ANZ Melbourne collaborated with Amazon to have a run for the go!!!
In this repo i will go through all my tries, though i lasted in 10th Position among 30 participants on 3rd-Feb-2020
Though this wasn't exactly the competitions with the same parameters
- We cant change the Actions parameters
- We can't change the Hyperparameters
- We had a limited temporary AWS account which expires the next day after the workshop.
The Track used was of Re-Invent2018
def straight_line_reward(current_reward, steering):
# Positive reward if the car is in a straight line going fast
if abs(steering) < 0.1:
current_reward *= 1.5
elif abs(steering) < 0.2:
current_reward *= 1.2
elif steering > 15:
current_reward *= 0.8
elif steering > 30:
current_reward *= 0.5
return current_reward
def direction_reward(reward, waypoints, closest_waypoints, heading):
import math
DIRECTION_THRESHOLD = 10
next_point = waypoints[closest_waypoints[1]]
prev_point = waypoints[closest_waypoints[0]]
# Calculate the direction in radius, arctan2(dy, dx), the result is (-pi, pi) in radians
direction = math.degrees(math.atan2(next_point[1] - prev_point[1], next_point[0] - prev_point[0]))
# Cacluate difference between track direction and car heading angle
direction_diff = abs(direction - heading)
# Penalize if the difference is too large
malus=1
if direction_diff > DIRECTION_THRESHOLD:
malus=1-(direction_diff/50)
if malus<0 or malus>1:
malus = 0
reward *= malus
return reward
import math
def reward_function(params):
'''
Use square root for center line
'''
track_width = params['track_width']
distance_from_center = params['distance_from_center']
steering = abs(params['steering_angle'])
speed = params['speed']
all_wheels_on_track = params['all_wheels_on_track']
reward = 0.001
if params['all_wheels_on_track']:
reward = 1 - (distance_from_center / (track_width/2))**(4)
if reward < 0:
reward = 0.001
reward = straight_line_reward(reward, steering)
reward = direction_reward(reward, params['waypoints'], params['closest_waypoints'], params['heading'])
reward *= (1+params['progress']/100)
else:
reward = 0.001
return float(reward)
def steering_reward(reward, steering, is_left_of_center, speed):
# For hard turn:
if steering < -25 and is_left_of_center == False and speed < 3 or steering > 25 and is_left_of_center == True and speed < 3:
reward *= 1.2
else:
reward *= 0.8
# For soft turn
if abs(steering) < 15 and speed < 4:
reward *= 1.1
else:
reward *= 0.9
# For straight
if abs(steering) < 0.1 and speed > 4:
reward *= 1.4
return reward
def direction_reward(reward, waypoints, closest_waypoints, heading):
import math
next_point = waypoints[closest_waypoints[1]]
prev_point = waypoints[closest_waypoints[0]]
# Calculate the direction in radius, arctan2(dy, dx), the result is (-pi, pi) in radians
direction = math.degrees(math.atan2(next_point[1] - prev_point[1], next_point[0] - prev_point[0]))
# Cacluate difference between track direction and car heading angle
direction_diff = abs(direction - heading)
# Penalize if the difference is too large
if direction_diff > 30:
reward *= 0.5
return reward
def reward_function1(params):
if params['all_wheels_on_track']:
reward = params['progress']/100
else:
reward = 0.001
return float(reward)
def straight_line_reward(current_reward, steering, speed):
# Positive reward if the car is in a straight line going fast
if abs(steering) < 0.1 and speed > 6:
current_reward *= 1.5
elif abs(steering) < 0.2 and speed > 5:
current_reward *= 1.2
return current_reward
def reward_function(params):
'''
Example of rewarding the agent to follow center line
'''
# Give a very low reward by default
reward = 1e-3
# Read input parameters
track_width = params['track_width']
distance_from_center = params['distance_from_center']
# Read input parameters
all_wheels_on_track = params['all_wheels_on_track']
steer = abs(params['steering_angle'])
steer_thres = 12
if all_wheels_on_track and (0.5*track_width - distance_from_center) >= 0.07:
if steer > steer_thres:
reward *= 0.8
else:
reward = 1.0
reward = direction_reward(reward,params['waypoints'],params['closest_waypoints'],params['heading']) + reward
reward = steering_reward(reward,steer,params['is_left_of_center'] , params['speed']) + reward
reward += reward_function1(params)
reward += straight_line_reward(reward, steer, params['speed'])
else:
reward = 1e-3
return float(reward)
def straight_line_reward(current_reward, steering):
# Positive reward if the car is in a straight line going fast
if abs(steering) < 0.1:
current_reward *= 1.5
elif abs(steering) < 0.2:
current_reward *= 1.2
elif steering > 15:
current_reward *= 0.8
elif steering > 30:
current_reward *= 0.5
return current_reward
def direction_reward(reward, waypoints, closest_waypoints, heading):
import math
DIRECTION_THRESHOLD = 10
next_point = waypoints[closest_waypoints[1]]
prev_point = waypoints[closest_waypoints[0]]
# Calculate the direction in radius, arctan2(dy, dx), the result is (-pi, pi) in radians
direction = math.degrees(math.atan2(next_point[1] - prev_point[1], next_point[0] - prev_point[0]))
# Cacluate difference between track direction and car heading angle
direction_diff = abs(direction - heading)
# Penalize if the difference is too large
malus=1
if direction_diff > DIRECTION_THRESHOLD:
malus=1-(direction_diff/50)
if malus<0 or malus>1:
malus = 0
reward *= malus
return reward
import math
def reward_function(params):
'''
Use square root for center line
'''
track_width = params['track_width']
distance_from_center = params['distance_from_center']
steering = abs(params['steering_angle'])
speed = params['speed']
all_wheels_on_track = params['all_wheels_on_track']
reward = 0.001
if params['all_wheels_on_track']:
reward = 1 - (distance_from_center / (track_width/2))**(4)
if reward < 0:
reward = 0.001
reward = straight_line_reward(reward, steering)
reward = direction_reward(reward, params['waypoints'], params['closest_waypoints'], params['heading'])
reward *= (1+params['progress']/100)
else:
reward = 0.001
return float(reward)
def steering_reward(reward, steering, is_left_of_center, speed):
# For hard turn:
if steering < -25 and is_left_of_center == False and speed < 3 or steering > 25 and is_left_of_center == True and speed < 3:
reward *= 1.2
else:
reward *= 0.8
# For soft turn
if abs(steering) < 15 and speed < 4:
reward *= 1.1
else:
reward *= 0.9
# For straight
if abs(steering) < 0.1 and speed > 4:
reward *= 1.4
return reward
def direction_reward(reward, waypoints, closest_waypoints, heading):
import math
next_point = waypoints[closest_waypoints[1]]
prev_point = waypoints[closest_waypoints[0]]
# Calculate the direction in radius, arctan2(dy, dx), the result is (-pi, pi) in radians
direction = math.degrees(math.atan2(next_point[1] - prev_point[1], next_point[0] - prev_point[0]))
# Cacluate difference between track direction and car heading angle
direction_diff = abs(direction - heading)
# Penalize if the difference is too large
if direction_diff > 30:
reward *= 0.5
return reward
def reward_function1(params):
if params['all_wheels_on_track']:
reward = params['progress']/100
else:
reward = 0.001
return float(reward)
def straight_line_reward(current_reward, steering, speed):
# Positive reward if the car is in a straight line going fast
if abs(steering) < 0.1 and speed > 6:
current_reward *= 1.5
elif abs(steering) < 0.2 and speed > 5:
current_reward *= 1.2
return current_reward
def reward_function(params):
'''
Example of rewarding the agent to follow center line
'''
# Give a very low reward by default
reward = 1e-3
# Read input parameters
track_width = params['track_width']
distance_from_center = params['distance_from_center']
# Read input parameters
all_wheels_on_track = params['all_wheels_on_track']
steer = abs(params['steering_angle'])
steer_thres = 12
if all_wheels_on_track and (0.5*track_width - distance_from_center) >= 0.07:
if steer > steer_thres:
reward *= 0.8
else:
reward = 1.0
reward = direction_reward(reward,params['waypoints'],params['closest_waypoints'],params['heading']) + reward
reward = steering_reward(reward,steer,params['is_left_of_center'] , params['speed']) + reward
reward += reward_function1(params)
reward += straight_line_reward(reward, steer, params['speed'])
else:
reward = 1e-3
return float(reward)
def steering_reward(reward, steering, is_left_of_center, speed):
# For hard turn:
if (steering < -25 and is_left_of_center == False) or (steering > 25 and is_left_of_center == True):
reward *= 1.2
else:
reward *= 0.8
# For soft turn
if abs(steering) < 15 and speed < 4:
reward *= 1.1
else:
reward *= 0.9
# For straight
if abs(steering) < 1 and speed > 4:
reward *= 1.4
return reward
def direction_reward(reward, waypoints, closest_waypoints, heading):
import math
next_point = waypoints[closest_waypoints[1]]
prev_point = waypoints[closest_waypoints[0]]
# Calculate the direction in radius, arctan2(dy, dx), the result is (-pi, pi) in radians
direction = math.degrees(math.atan2(next_point[1] - prev_point[1], next_point[0] - prev_point[0]))
# Cacluate difference between track direction and car heading angle
direction_diff = abs(direction - heading)
# Penalize if the difference is too large
if direction_diff > 30:
reward *= 0.5
else:
reward *= 1.1
return reward
def straight_line_reward(current_reward, steering, speed):
# Positive reward if the car is in a straight line going fast
if abs(steering) < 0.1:
current_reward *= 2
elif abs(steering) < 0.3:
current_reward *= 1.5
elif abs(steering) < 0.5:
current_reward *= 1.2
return current_reward
def reward_function(params):
'''
Example of rewarding the agent to follow center line
'''
# Give a very low reward by default
reward = 1e-3
# Read input parameters
track_width = params['track_width']
distance_from_center = params['distance_from_center']
# Read input parameters
all_wheels_on_track = params['all_wheels_on_track']
steer = abs(params['steering_angle'])
steer_thres = 12
if all_wheels_on_track and (0.5*track_width - distance_from_center) >= 0.05:
if steer > steer_thres:
reward = 0.8
else:
reward = 1.0
reward = direction_reward(reward,params['waypoints'],params['closest_waypoints'],params['heading'])
reward = steering_reward(reward,params['steering_angle'],params['is_left_of_center'] , params['speed'])
reward = straight_line_reward(reward, params['steering_angle'], params['speed'])
elif all_wheels_on_track and distance_from_center< track_width/4:
reward = steering_reward(reward,params['steering_angle'],params['is_left_of_center'] , params['speed'])
else:
reward = 1e-3
return float(reward)
- I went with the training time of 1 hour (Try 4) but it went downtracked for me, i should have submitted the models which at least ran 4hours - gave me best of 17.25 Seconds of round time
- Don't go with the reward graph and focus if you are completing the track 100%
- Don't clone the model, instead "Create a model" each time you need the clone a model.
- The guy who won ran it for almost 8 hours won the competition
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