Input Parameters of the AWS DeepRacer Reward Function - AWS DeepRacer

Input Parameters of the AWS DeepRacer Reward Function

The AWS DeepRacer reward function takes a dictionary object as the input.

def reward_function(params) : reward = ... return float(reward)

The params dictionary object contains the following key-value pairs:

{ "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 }

A more detailed technical reference of the input parameters is as follows.

all_wheels_on_track

Type: Boolean

Range: (True:False)

A Boolean flag to indicate whether the agent is on-track or off-track. It's off-track (False) if any of its wheels are outside of the track borders. It's on-track (True) if all of the wheels are inside the two track borders. The following illustration shows that the agent is on-track.


                    Image: AWS DeepRacer reward function input parameter of all_wheels_on_track = True.

The following illustration shows that the agent is off-track.


                    Image: AWS DeepRacer reward function input parameter of all_wheels_on_track = False.

Example: A reward function using the all_wheels_on_track parameter

define reward_function(params): ############################################################################# ''' Example of using all_wheels_on_track and speed ''' # Read input variables all_wheels_on_track = params['all_wheels_on_track'] speed = params['speed'] # Set the speed threshold based your action space SPEED_THRESHOLD = 1.0 if not all_wheels_on_track: # Penalize if the car goes off track reward = 1e-3 elif speed < SPEED_THRESHOLD: # Penalize if the car goes too slow reward = 0.5 else: # High reward if the car stays on track and goes fast reward = 1.0 return reward

closest_waypoints

Type: [int, int]

Range: [(0:Max-1),(1:Max-1)]

The zero-based indices of the two neighboring waypoints closest to the agent's current position of (x, y). The distance is measured by the Euclidean distance from the center of the agent. The first element refers to the closest waypoint behind the agent and the second element refers the closest waypoint in front of the agent. Max is the length of the waypoints list. In the illustration shown in waypoints, the closest_waypoints would be [16, 17].

Example: A reward function using the closest_waypoints parameter.

The following example reward function demonstrates how to use waypoints and closest_waypoints as well as heading to calculate immediate rewards.

def reward_function(params): ############################################################################### ''' Example of using waypoints and heading to make the car in the right direction ''' import math # Read input variables waypoints = params['waypoints'] closest_waypoints = params['closest_waypoints'] heading = params['heading'] # Initialize the reward with typical value reward = 1.0 # Calculate the direction of the center line based on the closest waypoints 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 track_direction = math.atan2(next_point[1] - prev_point[1], next_point[0] - prev_point[0]) # Convert to degree track_direction = math.degrees(track_direction) # Calculate the difference between the track direction and the heading direction of the car direction_diff = abs(track_direction - heading) if direction_diff > 180: direction_diff = 360 - direction_diff # Penalize the reward if the difference is too large DIRECTION_THRESHOLD = 10.0 if direction_diff > DIRECTION_THRESHOLD: reward *= 0.5 return reward

closest_objects

Type: [int, int]

Range: [(0:len(object_locations)-1), (0:len(object_locations)-1]

The zero-based indices of the two closest objects to the agent's current position of (x, y). The first index refers to the closest object behind the agent, and the second index refers to the closest object in front of the agent. If there is only one object, both indices are 0.

distance_from_center

Type: float

Range: 0:~track_width/2

Displacement, in meters, between the agent center and the track center. The observable maximum displacement occurs when any of the agent's wheels are outside a track border and, depending on the width of the track border, can be slightly smaller or larger than half the track_width.


                    Image: AWS DeepRacer reward function input parameter of distance_from_center.

Example: A reward function using the distance_from_center parameter

def reward_function(params): ################################################################################# ''' Example of using distance from the center ''' # Read input variable track_width = params['track_width'] distance_from_center = params['distance_from_center'] # Penalize if the car is too far away from the center marker_1 = 0.1 * track_width marker_2 = 0.5 * track_width if distance_from_center <= marker_1: reward = 1.0 elif distance_from_center <= marker_2: reward = 0.5 else: reward = 1e-3 # likely crashed/ close to off track return reward

heading

Type: float

Range: -180:+180

Heading direction, in degrees, of the agent with respect to the x-axis of the coordinate system.


                    Image: AWS DeepRacer reward function input parameter of heading.

Example: A reward function using the heading parameter

For more information, see closest_waypoints.

is_crashed

Type: Boolean

Range: (True:False)

A Boolean flag to indicate whether the agent has crashed into another object (True) or not (False) as a termination status.

is_left_of_center

Type: Boolean

Range: [True : False]

A Boolean flag to indicate if the agent is on the left side to the track center (True) or on the right side (False).

is_offtrack

Type: Boolean

Range: (True:False)

A Boolean flag to indicate whether the agent has off track (True) or not (False) as a termination status.

is_reversed

Type: Boolean

Range: [True:False]

A Boolean flag to indicate if the agent is driving on clock-wise (True) or counter clock-wise (False).

It's used when you enable direction change for each episode.

objects_distance

Type: [float, … ]

Range: [(0:track_length), … ]

A list of the distances between objects in the environment in relation to the starting line. The ith element measures the distance in meters between the ith object and the starting line along the track center line.

To index the distance between a single object and the agent, use:

abs(params["objects_distance"][index] - (params["progress"]/100.0)*params["track_length"])

Note

abs | (var1) - (var2)| = how close the car is to an object, WHEN var1 = ["objects_distance"][index] and var2 = params["progress"]*params["track_length"]

To get an index of the closest object in front of the vehicle and the closest object behind the vehicle, use the "closest_objects" parameter.

objects_heading

Type: [float, … ]

Range: [(-180:180), … ]

List of the headings of objects in degrees. The ith element measures the heading of the ith object. For stationary objects, their headings are 0. For a bot vehicle, the corresponding element's value is the vehicle's heading angle.

objects_left_of_center

Type: [Boolean, … ]

Range: [True|False, … ]

List of Boolean flags. The ith element value indicates whether the ith object is to the left (True) or right (False) side of the track center.

objects_location

Type: [(x,y), ...]

Range: [(0:N,0:N), ...]

List of all object locations, each location is a tuple of (x, y).

The size of the list equals the number of objects on the track. Note the object could be the stationary obstacles, moving bot vehicles.

objects_speed

Type: [float, … ]

Range: [(0:12.0), … ]

List of speeds (meters per second) for the objects on the track. For stationary objects, their speeds are 0. For a bot vehicle, the value is the speed you set in training.

progress

Type: float

Range: 0:100

Percentage of track completed.

Example: A reward function using the progress parameter

For more information, see steps.

speed

Type: float

Range: 0.0:5.0

The observed speed of the agent, in meters per second (m/s).


                    Image: AWS DeepRacer reward function input parameter of speed.

Example: A reward function using the speed parameter

For more information, see all_wheels_on_track.

steering_angle

Type: float

Range: -30:30

Steering angle, in degrees, of the front wheels from the center line of the agent. The negative sign (-) means steering to the right and the positive (+) sign means steering to the left. The agent center line is not necessarily parallel with the track center line as is shown in the following illustration.


                    Image: AWS DeepRacer reward function input parameter of
                        steering_angle.

Example: A reward function using the steering_angle parameter

def reward_function(params): ''' Example of using steering angle ''' # Read input variable steering = abs(params['steering_angle']) # We don't care whether it is left or right steering # Initialize the reward with typical value reward = 1.0 # Penalize if car steer too much to prevent zigzag STEERING_THRESHOLD = 20.0 if steering > ABS_STEERING_THRESHOLD: reward *= 0.8 return reward

steps

Type: int

Range: 0:Nstep

Number of steps completed. A step corresponds to an action taken by the agent following the current policy.

Example: A reward function using the steps parameter

def reward_function(params): ############################################################################# ''' Example of using steps and progress ''' # Read input variable steps = params['steps'] progress = params['progress'] # Total num of steps we want the car to finish the lap, it will vary depends on the track length TOTAL_NUM_STEPS = 300 # Initialize the reward with typical value reward = 1.0 # Give additional reward if the car pass every 100 steps faster than expected if (steps % 100) == 0 and progress > (steps / TOTAL_NUM_STEPS) * 100 : reward += 10.0 return reward

track_length

Type: float

Range: [0:Lmax]

The track length in meters. Lmax is track-dependent.

track_width

Type: float

Range: 0:Dtrack

Track width in meters.


                    Image: AWS DeepRacer reward function input parameter of track_width.

Example: A reward function using the track_width parameter

def reward_function(params): ############################################################################# ''' Example of using track width ''' # Read input variable track_width = params['track_width'] distance_from_center = params['distance_from_center'] # Calculate the distance from each border distance_from_border = 0.5 * track_width - distance_from_center # Reward higher if the car stays inside the track borders if distance_from_border >= 0.05: reward *= 1.0 else: reward = 1e-3 # Low reward if too close to the border or goes off the track return reward

x, y

Type: float

Range: 0:N

Location, in meters, of the agent center along the x and y axes, of the simulated environment containing the track. The origin is at the lower-left corner of the simulated environment.


                    Image: AWS DeepRacer reward function input parameters of x,y.

waypoints

Type: list of [float, float]

Range: [[xw,0,yw,0] … [xw,Max-1, yw,Max-1]]

An ordered list of track-dependent Max milestones along the track center. Each milestone is described by a coordinate of (xw,i, yw,i). For a looped track, the first and last waypoints are the same. For a straight or other non-looped track, the first and last waypoints are different.


                    Image: AWS DeepRacer reward function input parameter of waypoints.

Example A reward function using the waypoints parameter

For more information, see closest_waypoints.