What Is Reinforcement Learning?

Reinforcement learning is a form of machine learning (ML) that lets AI models refine their decision-making process based on positive, neutral, and negative feedback that helps them decide whether to repeat an action in similar circumstances. Reinforcement learning occurs in an exploratory environment as developers pursue a set goal, making it different from both supervised and unsupervised learning.

In reinforcement learning, the algorithm works with an unlabeled data set focused on a specific outcome. Every step taken by the algorithm to explore the data set creates feedback, either positive, negative, or neutral. That feedback is the “reinforcement” part of the learning process—as it accumulates, it supports the decision to either move forward with a positive path or avoid a negative path. Eventually, the model can determine the best strategy to achieve an outcome. Because the algorithm considers the bigger-picture primary goal, this path may include a process of delayed gratification, accumulating smaller negative consequences in order to achieve the desired outcome.

If this sounds familiar, it’s because reinforcement learning mimics the natural learning process. Praise and rewards along with negative consequences inform the boundaries of developing minds, reinforcing guidelines for interacting with and succeeding in the world, whether that involves a young animal hunting food or a human child learning to identify symbols. Because reinforcement learning works akin to real-world learning, it’s useful for complex and open-ended scenarios where longer-term strategy may be more important than an immediate outcome.

In environments filled with rules, limitations, and connected or dynamic relationships, reinforcement learning brings nuance to model decision-making by fostering an understanding of the consequences of actions. On a technical level, reinforcement learning provides much more flexibility than supervised learning because it doesn’t rely on labeled data sets. Instead, models learn through experimentation, creating an adaptability that leads to a broader range of solutions across an entire spectrum of success. The models can adapt to circumstances.

What Is Reinforcement Learning?

Reinforcement learning is where models refine their decision-making process based on positive, neutral, and negative reinforcement. It’s an effective choice for training machine learning models in several circumstances. Reinforcement learning is particularly appropriate when the goal is to understand strategies behind successful outcomes rather than produce more straightforward decision trees.

For example, if an AI model successfully completes a level in a game, it may be rewarded with bonus points or a level advancement. Neutral reinforcement, on the other hand, refers to situations where no rewards or penalties are given and is typically used when the model’s actions don’t have a significant impact on the overall goal or objective. Negative reinforcement involves penalties when the model performs undesirable actions or fails to achieve the desired outcome. For instance, if the AI makes a disallowed or unsuccessful move in a game, it may be penalized with a deduction in points or by being bumped down a level.

Use cases ideal for reinforcement learning include

• Gaming: The earliest computer chess opponents were built on a series of if/then rules. With reinforcement learning, the model receives a broader, more organic intake of situations, choices, and outcomes, creating a complex decision-making process that results in a more sophisticated CPU opponent.
• Generative AI: Reinforcement learning can be part of the ML foundation for a generative AI model. Whether the model generates images, text, or audio, reinforcement learning enables a trial-and-error approach to determine and refine the accuracy of prompts and outputs.
• Marketing: Every marketing engagement is a chance for reinforcement learning. Whether customers opened, clicked, and stayed on pages—or not—offers both positive and negative reinforcement, which feeds back into the model to create a more accurate customer profile.
• Recommendation engines: A recommendation model gets positive reinforcement through the engagement received for each suggestion. This leads to patterns that build up to a more precise model for customer profiles.
• Self-driving cars: By learning in controlled and simulated environments, self-driving car models can gain a depth of understanding for situationally complex circumstances. Because driving creates so many in-the-moment decisions with factors such as proximity, speed, weather, and hazards, reinforcement learning allows for a range of responses to refine decision-making in models.

In all of these cases, the initial stages of training are akin to a toddler beginning to understand the world. By the time the model reaches the production stage, it can be considered mature or adult, capable of making generally accurate decisions while continuously learning to refine that level of accuracy—and with the right circumstances and resources, even attain mastery of the topic, whether that’s playing a game such as chess or providing recommendations that always interest a customer.

Reinforcement Learning FAQs

Is reinforcement learning ML or AI?

Reinforcement learning is a machine learning technique that can be used to train systems to make decisions based on receiving positive, neutral, and negative feedback. An ML model using reinforcement learning can be part of a greater artificial intelligence model designed to simulate human reactions to a particular circumstance or situation.

What are the three main types of reinforcement learning?

The three main types of reinforcement learning are

• Model-based: An environment is created for the model to freely explore as it determines its parameters in order to craft the best path to success.
• Policy-based: The relationships between potential strategies (policies), actions (values), and results are examined before the model determines which policy achieves the highest level of success.
• Value-based: The current environment in relation to specific actions (values) is examined before the model determines which value achieves the highest level of success.

What’s the difference between supervised learning and reinforcement learning?

Supervised learning uses labeled data sets to train models so they can accurately achieve expected outcomes. Reinforcement learning uses a more exploratory approach, providing an open environment for the model to explore different strategies and choices until the desired outcome is met.