What Is Deep Reinforcement Learning?

Key takeaways

Deep reinforcement learning enables machines to use rewards and penalties to select the next best action to achieve a specific goal.

• Deep reinforcement learning works by using frameworks known as artificial neural networks. These networks build up layers of nodes that mimic how neurons function in your brain.

• Self-driving cars, automated robotics, and image processing are common applications of deep reinforcement learning.

• You can build expertise in deep reinforcement learning by immersing yourself in studying machine learning.

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What is deep reinforcement learning?

Deep reinforcement learning describes when a computer uses rewards and penalties to learn the best next action to achieve a specific goal. This process allows the computer to learn the same way humans do by taking in data and observing our environment before making a decision. Operating under conditions of uncertainty, these artificial neural networks (ANNs) use advanced algorithms to analyze vast datasets, allowing computers to learn, adapt, and evolve based on their results. This means that computers, much like humans, can learn, adapt, and change based on the results they receive.

Here is an example of reinforcement learning:

Imagine you’re sitting in front of a campfire for the first time. You place a marshmallow on a stick over the flames and watch it turn golden and gooey. After you eat it, you decide to place a second marshmallow over the flames using your fingers. The flames singe you, and you drop the marshmallow in the fire. The third time you put a marshmallow into the fire, you use a stick like the first time. While standard reinforcement learning can influence simple choices (like using a stick or your fingers), deep reinforcement learning uses artificial neural networks to handle complex situations with thousands of moving variables, like navigating a self-driving car through a busy intersection.

This scenario is an example of reinforcement learning, or the process of learning through rewards and penalties. For computers, deep reinforcement learning is a similar process of developing good or accurate decisions over time.

What is deep reinforcement learning used for?

Deep reinforcement learning finds uses across various industries to support and improve human activity. You’ve likely seen or even interacted with this technology in industries such as:

• Self-driving cars

• Natural language processing (NLP)

• Automated robotics

• Image processing

• Recommendation systems

Deep reinforcement learning finds use in industries where immense data sets are generated constantly, because these programs require huge volumes of information to run trial-and-error equations successfully.

How does deep reinforcement learning work?

Deep reinforcement learning works by using frameworks known as artificial neural networks. These networks build up layers of nodes that mimic how neurons function in your brain. The nodes process and pass information along the networks, using trial and error to discover accurate results.

In deep reinforcement learning, the computer develops a strategy based on feedback, and produces results as a policy. These policies inform themselves by the state of the computer, its current situation, and the different options the computer chooses from, which is called an action set. Selecting from these options allows the computer to consider different actions and observe the results of its different choices. Because deep reinforcement learning allows for the coordination of learning, decision-making, and representation, this technology may provide cognitive scientists with new insights into how the human brain functions.

Deep reinforcement learning is unique because the structure of the software provides the opportunity for it to learn much like your brain does. It comprises thousands of layers of neural networks that take in unlabeled, unstructured data and make sense of its contents without needing a human to direct the learning process.

Example

If your goal is to teach a robot to walk up a set of stairs, the computer might decide to take a step that ends up being too big. The resulting “punishment” of a fall is negative feedback that the computer uses to adjust its next step to a smaller one. Some scientists use virtual environments for the robot to learn so that it can test different options and fall repeatedly without risking damage to real, expensive robotics parts. When you combine the robot’s experience of trial-and-error reinforcement learning with artificial neural networks and new data integration of deep learning, you develop a deep reinforcement learning system.

Who uses deep reinforcement learning?

 Beyond data scientists and robotics engineers, a diverse range of professionals leverage deep reinforcement learning to solve complex, dynamic problems. Quantitative researchers in finance use it to build algorithmic trading systems and optimize investment portfolios, while healthcare data analysts apply it to customize patient treatment plans and accelerate drug discovery. Additionally, game developers utilize the technology to train non-player characters (NPCs) that adapt to a player’s style, and sustainability engineers deploy it to autonomously manage energy consumption in massive data centers.

Pros and cons of using deep reinforcement learning

Some pros of using deep reinforcement learning surface in various industries—such as business and health care—that you might interact with daily. For businesses, deep reinforcement learning allows your company to create optimized workflows that are accurate and reflect the nuances of your particular business. As technology advances, you’ll see more personalized media recommendations, more accurate language translations, and safer self-driving cars. Deep reinforcement learning is key to advancing artificial intelligence (AI) and its ability to support and improve the human experience in health care, marketing, technology, and more.

A con of deep reinforcement learning is that the software system requires an immense amount of data. This data might be expensive to gather and store, and if it’s not valuable or large enough, it might result in inaccurate or non-optimal results and insights.

How to get started in deep reinforcement learning

If you’re interested in learning more about deep reinforcement learning, the first step is to look for online guides, courses, and resources. These opportunities give you the chance to practice with deep reinforcement-learning tutorials and algorithms.

One example of a career that includes deep reinforcement learning is a machine learning engineer. In this position, you would create artificial intelligence programs designed to run independently of human involvement. Typically, you would work with teams of other data professionals. To become a machine learning engineer, you’ll most likely need a bachelor’s degree in a subject such as computer science. The median total salary of a machine learning engineer in the US is $162,000 per year [1]. This figure includes base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

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