Have you ever wondered how self-driving cars learn to navigate complex environments? How robots learn to perform tasks that humans take for granted? How games like AlphaGo and Atari Breakout learn to master complex strategies?
If you are curious about these questions, then you can get the answers and more by diving into the fascinating world of reinforcement learning, a branch of artificial intelligence that teaches machines to learn from their own actions and rewards.
But wait, there’s more. You can also experience the thrill of reinforcement learning in action by participating in AWS DeepRacer, a global racing league that challenges you to build and train autonomous racing cars using AWS cloud services.
In this article, I will share with you what reinforcement learning is, how it is related to AWS DeepRacer, and how you can get started with this exciting and rewarding learning journey.
In the end I will also give you link to an exciting and free browser-based game that teaches Deepracer and ML basics.
What is Reinforcement Learning?
Reinforcement learning (RL) is a type of machine learning that enables an agent (such as a robot or a game character) to learn from its own actions and the feedback it receives from the environment. The agent does not have any prior knowledge or instructions on how to perform a task. Instead, it learns by trial and error, exploring different actions and observing the outcomes.
The agent’s goal is to maximize the cumulative reward it receives over time. The reward is a signal that indicates how well the agent is performing the task.
To understand the RL process, let’s imagine an agent learning to play a platform game:
source: huggingface deep-rl-course
Our Agent receives state S0 from the Environment — we receive the first frame of our game (Environment).
Based on that state S0, the Agent takes action A0 — our Agent will move to the right.
The environment goes to a new state S1 — new frame.
The environment gives some reward to the Agent — we’re not dead (Positive Reward +1).
This RL loop outputs a sequence of state, action, reward and next state. The agent’s goal is to maximize its cumulative reward, called the expected return.
How is Reinforcement Learning related to AWS DeepRacer?
In reinforcement learning for AWS DeepRacer, an agent (vehicle) learns from an environment (a track) by interacting with it and receiving rewards for performing specific actions.
The model training process will simulate multiple experiences of the vehicle on the track in an attempt to find a policy (a function mapping the agent’s current state to a driving decision) which maximizes the average total reward the agent experiences.
After training, you will be able to evaluate the model’s performance in a new environment, deploy the model to a physical vehicle, or submit the model to a virtual circuit.
This GIF explains what the agent, environment, action, state, and reward are within the AWS DeepRacer context.
But what exactly is AWS DeepRacer and how can you get started.
AWS DeepRacer is a 1/18th scale autonomous racing car that you can use to learn and apply reinforcement learning in a fun and engaging way. AWS DeepRacer is powered by AWS cloud services, such as Amazon SageMaker, AWS RoboMaker, Amazon S3, and more.
If you want to dive deep, you may refer to my other articles on AWS DeepRacer
Official Website- Racing Simulator Software — AWS DeepRacer — AWS (amazon.com)
AWS DeepRacer Arcade for mobile, a free browser-based game that teaches ML basics. https://go.aws/3P6HuUR