Reinforcement Learning: Applications and Real-World Examples

Last Updated on March 10, 2023

Reinforcement Learning

Reinforcement learning is a subfield of artificial intelligence (AI) that focuses on how machines can learn to make decisions by interacting with their environments. In reinforcement learning, an agent learns to maximize a reward signal over time by taking actions that lead to desirable outcomes.

This differs from supervised learning, where the machine is trained on labeled data, and unsupervised learning, where the machine is trained on unlabeled data.

Reinforcement learning has become increasingly important in today’s world, with applications ranging from robotics and automation to finance and trading, health care, and advertising and recommendation systems.

Applications of Reinforcement Learning

Robotics and Automation

Reinforcement learning has been particularly useful in the field of robotics and automation. Through it, robots can learn to perform complex tasks by trial and error.

For example, self-driving cars use it to learn how to navigate roads safely and efficiently. By training the car in a simulated environment, it can learn to recognize different objects, such as traffic lights, and respond accordingly.

Warehouse automation is another good example of improved efficiency. By training robots to learn how to navigate warehouses and pick up items, companies can reduce costs and improve productivity.


By using reinforcement learning, machines can learn to play games at a level that exceeds human performance.

AlphaGo, developed by Google DeepMind, is a prime example of this. AlphaGo was able to defeat the world champion in the ancient Chinese game of Go, which had previously been considered too complex for machines to master.

AlphaZero, another program developed by Google DeepMind, was able to learn how to play chess, shogi, and Go at a superhuman level without any prior knowledge of the games.

Finance and Trading

Algorithmic trading, which involves using algorithms to buy and sell financial instruments, is a prime example. By using reinforcement learning, machines can learn how to identify profitable trading strategies and execute them automatically. This has led to increased efficiency and reduced costs in the financial industry.

Health Care

Reinforcement learning has the potential to revolutionize health care by improving the accuracy of diagnosis and treatment. By training machines on large datasets of medical records, machines can learn to identify patterns and make predictions about patient outcomes. This can lead to more personalized treatment plans and more effective drug discovery.

Advertising and Recommendation Systems

Reinforcement learning is being used to improve advertising and recommendation systems. With it, machines can learn to predict user preferences and suggest products or services that are likely to be of interest. This has led to increased revenue for companies such as Netflix and Amazon.

Natural Language Processing

Machine translation, question answering, predictive text, and text summarization are a few instances of natural language processing (NLP) that makes use of reinforcement learning. RL agents can imitate and anticipate how people talk to one another on a daily basis by studying common language patterns. This encompasses the actual language used as well as diction and syntax (the way words and phrases are put together) (the choice of words).

Traffic Light Control

Traffic congestion has become a major issue, particularly in metropolitan regions, as a result of increased urbanization and a rise in the number of cars per household.

A popular data-driven method for adaptive traffic signal management is reinforcement learning. These models are trained with the intention of learning a strategy that, given the present state of the traffic, controls the traffic light optimally.

The decision-making process must be dynamic and dependent on the volume of traffic coming in from various locations at various times of the day. Due to this non-stationary behavior, the traditional method of handling traffic appears to have its limitations. Additionally, the policy cannot be applied to an intersection with y lanes after it has been taught for an intersection with x lanes.

Real-World Examples of Reinforcement Learning

1. Autonomous driving with Wayve
Autonomous driving

In the past, methods for developing self-driving cars required defining logic principles. Scaling this up to the countless scenarios that autonomous vehicles might face on public roads can be challenging. Deep reinforcement learning may be useful in this situation.

Since 2018, the UK-based firm Wayve has been testing self-driving cars on public roads. In their article, “Learning to Drive in a Day,” they explain how they trained a model using deep reinforcement learning and a monocular picture as input. The distance the car traveled without the safety driver taking over was the prize. The model was developed in a driving exercise before being put to use on a 250-meter stretch of road in the real world.

They assert that, despite the development of their autonomous car technology, reinforcement learning still contributes to motion planning (ensuring the existence of a feasible path between the target and destination points).

2. Improving search engine results with
search engine results with

For on-site search inquiries, is an AI search engine. They enhance their search ranking algorithm using both “learn-to-rank” and reinforcement learning methods.

The use of a machine learning model learned on a dataset of query-result pairs scored according to their relevance is required for learn-to-rank. This method’s static inputs (the scores for the query-result combination) are one of its drawbacks.

Reinforcement learning uses feedback from clicks, purchases, signups, and other actions to gradually improve the search algorithm. The difficulty with applying reinforcement learning in this situation is that the quality of search results frequently starts out low and requires time and data before it begins to satisfy customer expectations.

3. Personalizing your Netflix recommendations
Netflix recommendations

Over 190 nations have 200 million subscribers to Netflix. Netflix seeks to show the most engaging and pertinent videos to each of these users. As the Director of Machine Learning and Recommender Systems at Netflix, Justin Basilico explains in his presentation titled “Netflix Explains Recommendations and Personalization,” they accomplish this by combining four key approaches: deep learning, causality, bandits & reinforcement learning, and objectives.

It is difficult to train a model that prioritizes long-term user happiness over short-term gratification. Exploration is a component of reinforcement learning that the model can use to gradually learn about new hobbies.

Justin points out that the high dimensionality and sizable problem space make it difficult to implement reinforcement learning in this situation. The crew created Accordion, a long-term training simulator, to assist with this.

4. Trading on the financial markets with IBM’s DSX platform
Trading on the financial markets

The elevated financial risks have discouraged the financial sector from implementing machine learning. IBM discusses a trading system that was reinforced learning-trained in this article.

The benefit of reinforcement learning in this situation is the capacity to develop forecasting skills that take into consideration any market effects the algorithm’s actions may have had. This feedback cycle enables the algorithm to self-tune as time passes, continuously improving its strength and adaptability. The profit or loss earned in each trade determines how the reward system works.

The model was evaluated using ARIMA-GARCH and a buy-and-hold approach (a forecasting model). They discovered that the model could accurately reproduce head-and-shoulder patterns, which is no small accomplishment.

Challenges and Limitations of Reinforcement Learning

While reinforcement learning has many potential applications, there are also several challenges and limitations to its use.

A. Sample inefficiency is one of the main challenges of reinforcement learning. This refers to the fact that machines require a large number of examples to learn effectively. In some cases, this may not be feasible, particularly in fields such as health care where the number of available examples may be limited.

B. Exploration vs. exploitation tradeoff is another challenge of reinforcement learning. This refers to the fact that machines need to balance the exploration of new actions with the exploitation of actions that have been successful in the past. This can be difficult, particularly in complex environments where the optimal actions may be difficult to identify.

C. Reward hacking and unintended consequences are also potential challenges of reinforcement learning. This refers to the fact that machines may learn to exploit loopholes in the reward signal to achieve their goals. This can lead to unintended consequences, such as machines learning to cheat or manipulate the system.

D. Ethics and fairness concerns are more potential limitations of reinforcement learning. Machines may learn biases from the data they are trained on, leading to unfair or discriminatory outcomes. Developers need to be aware of these potential biases and take steps to mitigate them.


Reinforcement learning is a powerful tool with many potential applications in a variety of fields. From gaming and finance to health care and advertising, reinforcement learning is being used to improve efficiency, accuracy, and profitability. However, there are also several challenges and limitations to its use that must be considered.

As it continues to evolve and improve, we will likely see even more applications and examples in the future. By understanding the strengths and limitations of reinforcement learning, we can continue to develop and refine this powerful technology to benefit society as a whole.

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