9 Real-Life Reinforcement Learning Examples and Use Cases

According to IBM, the next major step in artificial intelligence (AI) is creating autonomous agents.¹ These are AI programs that can act and respond to their environment without direct human supervision. AI reinforcement learning (RL) is a type of machine learning that enables AI agents to learn from trial and error through a system of rewards and punishments. This method resembles one of the ways that humans learn. It has many practical applications across industries, from autonomous cars to energy conservation to healthcare.²

This blog will explore nine reinforcement learning examples that illustrate how AI systems solve real-world problems.

What Is Reinforcement Learning?

Reinforcement learning is a method of training software to make optimal decisions based on its goals. Good decisions are rewarded, and bad decisions are ignored. An RL model can explore complex environments and receive feedback based on its actions. Because it’s set up to maximize long-term rewards, an RL model is ideal in situations that have long-term implications, such as energy consumption.³

The Markov decision process is a mathematical model of decision making that underpins RL. It's a method of describing how the agent makes decisions in an environment. As the agent moves through the environment, it creates its own rules and uses them to decide what action to take next. An example is a robot trying to navigate around a house without bumping into objects. As it moves around, it receives a reward for successfully avoiding objects and, as a result, learns from past actions.³

Reinforcement Learning Examples and Applications

Reinforcement learning has tremendous potential to create effective autonomous systems. Some experts believe that as RL models become more sophisticated, they’ll be used to create artificial general intelligence (AGI)–AI programs that are capable of performing any intellectual tasks that humans can.⁴ Currently, these systems are being used or developed for the following use cases.

1. Automated Robots

Robots trained in RL can learn to navigate an environment, grasp objects, and move them. Automated robots can perform tasks that are too dangerous or impossible for humans. They can also perform some tasks faster and more efficiently than humans, such as scanning warehouse inventory and other aspects of supply chain optimization.⁵

Automated robots can also work together in multi-agent systems to incorporate data from the entire supply chain and use it to make strategic decisions about issues such as sourcing raw materials, replenishing stock, and selecting suppliers.⁶

2. Financial Trading

Financial analysts can use reinforcement learning to drive profitable trading strategies. The most innovative methods use multi-agent systems to improve strategies by simulating multiple scenarios in a market environment. In this multi-agent reinforcement learning (MARL) framework, each agent represents a particular strategy or element of the market. The model uses interactions between agents to understand the interrelated elements of financial markets and optimize trading outcomes.⁷

3. Marketing and Advertising

Marketers test multiple versions of a campaign to determine which is most effective. Reinforcement learning automates this process, and agents can automatically allocate more traffic to higher-performing versions in real time. As a result, marketers don’t have to make manual adjustments based on data to immediately benefit from the most effective strategies. Amplify Analytix, for example, has incorporated RL into its Google Ads assistant, SOLD!, to maximize the return on investment (ROI) for marketers.⁸

4. Image Processing

When asked to process an image, RL agents will search the entire image as their starting point, then identify objects sequentially until all aspects are registered. Artificial vision systems also use deep convolutional neural networks, made up of large, labeled datasets, to map images to human-generated scene descriptions from simulation engines.⁹

Some examples of reinforcement learning in image processing include the following:¹⁰

• Mapping the environment for autonomous vehicles
• Identifying anomalies on diagnostic medical images
• Classifying images by detecting objects

5. Everyday Technology

You’ve probably encountered RL models in technology used in recommendation systems. Companies such as Yahoo and Netflix use RL to improve the news articles and shows they recommend to their users. These types of recommendation engines have typically been driven by historical data and item popularity, which can overlook new options that users may like. RL models avoid this shortcoming by focusing on long-term rewards.¹¹

6. Gaming

Gaming has been one of the primary use cases for RL in the past. Many of the developments that have come from the gaming industry, such as natural language processing and intelligent agents, are now being applied to wider use cases. Compared to traditional video games that rely on complex rules to control user behavior, training an RL model is much simpler. The agent learns directly in the simulated game environment through moving around in the game, defending itself against attackers, and creating effective strategies to win. RL agents are also used in bug detection and game testing because they can run multiple iterations without human input.¹²

7. Energy Conservation

RL agents can automatically adjust commercial building systems, such as HVAC, to conserve energy based on building occupancy, weather, and energy prices. They can also optimize energy generation from renewable sources such as solar and wind systems. RL agents make decisions about when to store energy and when to release it into the renewable energy grid based on supply and demand.¹³

8. Traffic Control

Engineers have developed RL traffic control models that adjust traffic signals to reduce delays, travel time, and queue lengths based on real-time traffic data. In one study, this kind of model was shown to significantly reduce travel time, vehicle waiting times, and vehicle queue lengths. This constituted an improvement over previous adaptive methods that relied primarily on historical data.¹⁴

9. Healthcare

RL agents can assist healthcare providers by optimizing clinical decision-making in complicated and rapidly changing settings. The agent can suggest personalized treatment plans that adapt based on the patient’s response. For example, it can adjust sedative dosages for patients in the ICU, personalize chemotherapy regimens for cancer patients, and automatically handle insulin dosages for patients with diabetes.¹⁵

Where Is Reinforcement Learning Headed?

While reinforcement learning applications in many industries are still nascent, many industries—including finance, automotive, healthcare, gaming, and robotics—are poised to be transformed by RL.¹⁶ There is ample opportunity for growth and unlimited application potential. RL models that incorporate deep learning technology can lead to more independent and flexible systems. New models are likely to involve more transfer learning integration, where models can retain previously learned knowledge, and multi-agent algorithms to promote collaboration and competition.

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