AWS DeepRacer 1O1

AWS DeepRacer is the fastest way to get rolling with machine learning. It is a fully autonomous 1/18th scale race car driven by reinforcement learning, 3D racing simulator and global racing league. Developers can train, evaluate, and tune RL models in the online simulator, deploy their models onto AWS DeepRacer for a real-world autonomous experience.

What is Reinforcement Learning?

Reinforcement learning is a type of dynamic programming that trains algorithms using a system of reward and punishment. Q-learning and SARSA (State-Action-Reward-State-Action) are two commonly used model-free RL algorithms. A reinforcement learning model will learn from its experience and over time will be able to identify which actions lead to the best rewards.

In reinforcement learning, an agent interacts with an environment with an objective to maximize its total reward. The agent takes an action based on the environment state and the environment returns the reward and the next state. The agent learns from trial and error, initially taking random actions and over time identifying the actions that lead to long-term rewards.

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In AWS DeepRacer, the reward function is a Python function which is given certain parameters that describe the current state and returns a numeric reward value. The parameters passed to the reward function describe various aspects of the state of the vehicle, such as its position and orientation on the track, its observed speed, steering angle and more.

We will talk about some of these parameters:

Heading

Waypoints

Track width

All wheels on track

Speed

Steering angle

Distance from center line

Position on track

1. Heading: The heading parameter describes the orientation of the vehicle in degrees, measured counter-clockwise from the X-axis of the coordinate system.
2. Waypoints: The waypoints parameter is an ordered list of milestones placed along the track center. Each waypoint in waypoints is a pair [x, y] of coordinates in meters, measured in the same coordinate system as the car’s position.
3. Track width: The track_width parameter is the width of the track in meters.
4. All wheels on track: The all_wheels_on_track parameter is a boolean (true/false) which is true if all four wheels of the vehicle are inside the track borders, and false if any wheel is outside the track.
5. Speed: The speed parameter measures the observed speed of the vehicle, measured in meters per second.
6. Steering angle: The steering_angle parameter measures the steering angle of the vehicle, measured in degrees.
7. Distance from center line: The distance_from_center parameter measures the displacement of the vehicle from the center of the track.
8. Position on track: The parameters x and y describe the position of the vehicle in meters, measured from the lower-left corner of the environment.

Do you want to start with AWS DeepRacer — AWS DeepRacer League is there for you.

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Keep Exploring !!! Keep Learning !!!

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