All You Need to Know about Reinforcement Learning

Reinforcement learning (RL) is the area of machine learning that is concerned with how software is able to take the right decision. The computer employs a trial and error method in order to find the best possible behavior or path that should be taken in a specific situation. Reinforcement learning is more or less a game-like situation. In order to enable the machine to do what is desired by the programmer, the artificial intelligence gets either rewards or penalties for the actions it performs. Ultimately, the goal is to maximize the rewards. Needless to say, this aspect of machine learning is the most effective way to hint at a machine's creativity as of now.

Consider an example wherein an autonomous vehicle is to be designed with a focus on parameters like minimizing the ride time, reducing pollution, obeying the traffic rules, etc. While coming up with this model, rather than relying on lengthy 'if-then' statements, reinforcement learning serves to be a savior. Here, the programmer would focus on preparing the reinforcement learning agent capable enough of learning from the system of rewards and penalties.

How to get into this field?

This is one of the most exciting fields that has garnered attention from so many. For the ones looking to make a career in this, there's no better time than now to get started.

AWS Deepracer

Signing up for Deepracer will give you an access to a simulator. This further allows the user to select a track, code a reward function, and also adjust tuning parameters. With a default reward function with tuning parameters, one can start training the racer followed by evaluating its performance. AWS Deepracer is undoubtedly a great tool to get started and get acquainted with RL.

Real-world examples

The best way to learn anything is to see how it applies to the real-world. With tons of real-world examples of business, academics, and government organizations experimenting as well as succeeding with this innovative aspect of machine learning, a lot can be learned and understood. Some real-life examples that can help in this aspect are – a grocery store that employs a personalized and recommendation engine that uses reinforcement learning, army deploying vehicles in different parts of the battle area, a robot playing sports, to name a few.

Books

Needless to say, books are certainly one of the best ways to gain knowledge and expertise no matter what the field is. Some of the best books and papers to get going are –

1. The Basics of Deep Q Learning: With this book, one can surely gain command on Math and processes of Reinforcement Learning.
2. The Hierarchical Learning paper: This is handy especially for those who want to understand Hierarchical Learning in detail.
3. Deep RL: As evident as the name, this delves deeper into the subject.

Applications of RL

Some of the applications of RL are in the areas of –

• Robotics
• Bidding & Advertising
• Augmented NLP
• Industrial Operations
• Supply Chain & Logistics
• Traffic Control
• Load Balancing

Challenges in RL

• Scaling and tweaking the neural network that controls the agent
• The agent performs the task as it is and not in the optimal or required way.
• Preparing the simulation environment is yet another challenge faced.

The crux of RL is how the agent is trained. Reinforcement learning is, without a doubt, cutting-edge technology and has the potential to transform the world. It is one of those ways that can make a machine creative