Reinforcement Learning: How It Works & Practical Applications

Reinforcement learning (RL) is an effective method when it comes to training AI models. It uses something called an agent, which runs through a series of actions or tasks. As the agent progresses through these tasks, it is rewarded when it completes an objective. This reinforces the positive outcomes and teaches the agent that the approach it used was the right one. 

If the agent fails at a task, it is not rewarded, or in some cases, it is penalized. The agent then keeps track of its performance history and uses these experiences to get better and better, until it has mastered the tasks. It isn’t all smooth sailing though, as you need to choose the right method for your application. 

In this article, we will explore exploration, exploitation, scalability and sparse rewards, how they are used with RL, and how they are used in the process of training. 

Understanding the Basics of Reinforcement Learning (RL)

Before we dive deeper into the topic, let’s look at basic concepts in RL. Here is an example of an RL objective, along with some of the steps that are required and how they would work. 

In our example, we have an agent that is given the task of finding its way around a blueprint of a building. To start with, we simply want to train this agent (think of it as a virtual robot) to find its way around the map as quickly as possible. The agent has something called a “state,” which is a descriptor about where it is on the map. While it is performing these tasks, it is making decisions that are called “actions,” such as “turn right,” “go back,” and “move forward.” As the training continues, the agent will be rewarded as it makes progress, which reinforces the positive behavior. 

If the agent gets stuck or it collides with walls, it receives negative feedback, which teaches the agent not to do those things. The main objective is for the agent to learn a cohesive set of strategies and actions, called a “policy,” which will inform its actions when certain conditions are met.  

What Are the Practical Applications of Reinforcement Learning

Here are some real-life examples of how RL has previously been used as a problem-solving technique and as a way to improve existing industries: 

• Robotics applications. RL has enabled robotics, such as factory welders, assembly line robots and even autonomous vehicles to perform dynamic tasks that require precision and repetition. Robots use RL to learn how to walk, interact with objects and navigate their environments. RL helps to reduce errors and improve efficiency.

• Gaming and entertainment. RL is used extensively when training agents in popular video games, and it incentivizes successful new ideas while correcting poor ones with negative outcomes.

• Trading and finance. RL has been used when training trading bots that operate on stock markets and crypto markets. These agents are trained on many different data sets and must make profitable trades to receive positive reinforcement for their strategies. Some finance companies use RL agents to help test theories without spending any real money, while others use agents in real world trades to stay ahead of the competition.

Challenges of Reinforcement Learning

RL does present some difficulties that need to be overcome for the solution to work properly, including:

• Balancing exploration and exploitation. Finding the right balance between these two functions is important in RL. With too much exploration (trying out new ideas and actions) there is a higher probability of mistakes and bad outcomes. If exploitation (using knowledge that is already known to the agent) is used too much, you run the risk of your agent not iterating fast enough or at all. That means that progress can be slowed or stopped all together.

• Sparse rewarding systems. If you are training a system that is not giving enough opportunities for you to provide feedback, or if providing feedback takes a long time, you will have problems steering your agent. In these situations, you must try something called reward shaping, which is when you give your agent smaller rewards more often as it completes smaller tasks. This positive reinforcement helps the agent get to a solution faster.

We hope that this has been a helpful look into how RL works. There are many different places where RL can be used, but this should give you a general enough idea to start you off on your own voyage of discovery if you want to start training an AI agent yourself.

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