Large language models don’t emerge fully formed. They must be molded for their intended purpose—and that intended purpose can dictate how a model is trained, from simple static pattern recognition to more dynamic reinforcement learning.

For a straightforward use case, traditional machine learning trains models on historical data to help them recognize patterns and generate outputs accordingly. Once those models are deployed, however, they generally don’t change much unless retrained, so their value and capabilities may remain static.

Reinforcement learning introduces a more dynamic approach that can continue to evolve based on human or agentic AI feedback. In essence, this ongoing learning process allows an AI platform to mature and improve in a manner comparable to skill development.

Models built and trained with reinforcement learning can complement their knowledge base with live data from their operating environment. It doesn’t do this by nature, of course; developers must build in parameters, rules, security, and governance to guide the model in its learning. With these robust measures in place, the model can receive feedback on its performance—either from a human, which is known as reinforcement learning from human feedback (RHLF), or through more advanced agentic AI-driven reinforcement learning—to assess how well it’s performing, and then adjust its parameters accordingly.

It’s both straightforward and requires complex thinking, but this guide covers what you need to know about reinforcement learning—from the core principles to use cases and even the future of this technology.

What is reinforcement learning and how does it work?

When we talk about reinforcement learning in AI, we’re referring to an approach that allows AI agents to learn to make better decisions by interacting with their environment. Because the agents receive feedback from their actions either from human training or automated processes, they can learn to tell whether they’re “missing the mark”—how close they are to a ‘good’ answer—and so get better at generating target results over time.

Let’s look at a potential reinforcement learning example using RHLF versus an AI agent to assist with using generative AI in marketing.

Once you’ve given your models access to the relevant data (which can include analytics, user inputs, and sensor signals), reinforcement learning may follow something like this cyclical process.

Observing the environment. When the agent is triggered to act, it will look into the available data (a mix of historical and live data) for context. The more it can interact with its environment—by taking actions based on observed data, receiving feedback in the form of rewards or penalties, and using that feedback to improve its future decisions—the better it can optimize its actions to achieve its objectives. This step also helps to establish a baseline that allows the agent to evaluate the impact of its actions. With RHLF involved, that feedback could also include humans fine-tuning outputs through the process.
Choosing an action. Using the provided data, the agent will choose a suitable action from a set of possible options, based on a strategy learned from past experiences and/or RHLF, to maximize its reward and further its objectives. For example, analyzing the data may suggest it should adjust the budget of a PPC campaign to help reach goals. While there’s an element of exploration (random actions), the decision isn’t arbitrary; it’s based on training, RHLF, and past outcomes to achieve a solid chance of success.
Reviewing feedback indicators. Once the agent has acted, it will monitor key metrics—such as impressions, clicks, and conversion rates—that reflect the impact of that action. It will then determine how well the action contributed to achieving the desired outcomes. It’s a good idea to keep humans in the loop here, too, to have oversight of these AI reflections.
Tweaking the decision-making framework. The agent will either make recommendations or automatically tweak its approach to further actions using the insights gained from the feedback. This process seeks to adjust the agent’s decision-making policy so that future actions lead to better results.

By repeating this four-step cycle over and over again, an AI agent can become more adept at working to fulfill its core goals.

What is the difference between reinforced, supervised, and unsupervised learning?

Reinforcement learning is all about the model learning from its experience processing data and generating answers. It helps enable AI agents to constantly improve based on collected information, which means reinforcement learning systems can operate with a high degree of autonomy once security and governance guardrails are in place. An agent capable of reinforcement learning can be put to work and left to refine itself mostly autonomously, though likely with some human reviews still in place.

Supervised learning, on the other hand, works by example. If you want a model to generate good poetry, you can train it on excellent poems and well-written prompts that will lead to the generation of similar pieces. Cover enough examples and you can forge a model capable of consistently getting high-quality results.

Lastly, unsupervised learning models are fed unlabeled data to identify patterns and relationships without using predefined categories. This approach is most useful for analytical purposes, such as looking for grammatical patterns within a vast dataset.

These methods are not inherently competitive, as each has its ideal use cases. If you’re looking for continuous adaptation, reinforcement machine learning is the way to go. If you have a wealth of great examples to draw upon, supervised learning can accelerate the production of strong results. And if you’re only interested in analyzing data, unsupervised learning can have you covered.

Types of reinforcement learning

There are various forms of AI reinforcement learning, each one designed to tackle a unique set of challenges. Here are some learning styles to look out for:

Model-free reinforcement learning: Without explicitly understanding or predicting how the environment works, this type of reinforcement learning focuses purely on learning which actions yield the best rewards. It maintains a laser-like focus on taking actions that will lead to results in the specific areas it was designed to target.
Model-based reinforcement learning: By creating a virtual map of its environment to understand the broader context, the AI agent can predict the outcomes of its actions more accurately. This helps avoid negative consequences in complex systems.
On-policy reinforcement learning: Here, the agent learns solely from the actions it performs in real time. It acts, observes the impact of its actions, and adjusts its strategy accordingly. This ensures a strong focus on the assigned task, but it can require multiple cycles to recalibrate effectively.
Off-policy reinforcement learning: Off-policy reinforcement learning allows the agent to learn from past experiences, including historical data or data from simulations, rather than only using real-world feedback. This can greatly accelerate the learning process by letting the agent gather knowledge ahead of deployment, but it can also muddy the waters and subject the agent to information that isn’t fully relevant to its primary goals.
Deep reinforcement learning: As the name suggests, deep reinforcement learning pairs deep learning with reinforcement learning, using deep learning architectures to tackle complex tasks like game playing or robotic control. This type of reinforcement learning uses greater processing power to approach advanced decision-making, using experiments like LLM reasoning to explore better alignment with human intentions and enhance real-world applications.
Multi-agent reinforcement learning: Multi-agent reinforcement learning involves deploying several agents capable of reinforcement learning within one system (or connected systems). These agents learn from their own experiences as well as each other’s, which can be particularly advantageous in tasks that require good coordination.

Applications of reinforcement learning

Reinforcement learning has incredible potential to improve systems, and it’s already making its mark across various industries. We’ll look at how it’s being used in some key sectors to optimize the use case, though with the caveat that these AI models may still need to be closely monitored and governed by humans with specialist knowledge.

Healthcare and life sciences

Reinforcement learning can aid the transformation of patient care by empowering deep probing and modeling of treatment options, as well as important deep work in healthcare research and development. Through iterative learning and constant reinforcement, a healthcare LLM can become more robust in its output. Medical facilities could then process and use information much more efficiently and effectively, taking some stress away from frontline medical staff.

Finance

Finance AI models can watch how the market fluctuates and process market patterns—this is a given. Reinforcement learning comes in when humans or agents tell the model which fluctuations are good and which are bad, which ones to pay attention to and which to ignore. This helps to make any AI-driven recommendations more robust for the specific use case. Imagine an AI agent attached to a portfolio with profit targets and acceptable risk levels; with reinforcement learning it can study the market movements of that portfolio and automatically adjust investments within the stated parameters.

Energy and utilities

Reinforcement learning excels at optimizing systems, which is crucial in the energy sector where even small improvements can reduce costs, minimize waste, and cut emissions. AI agents can track real-time data from sources, including meteorological forecasts and power grid analytics, and adjust energy storage and distribution dynamically.

Public sector

For operations tasked with monitoring public safety, speed and efficiency are essential—particularly when resources are stretched thin. AI systems can process vast amounts of data from surveillance cameras and online signals to rapidly determine when and where resources should be deployed. This helps to reduce misallocation and address issues early on to prevent escalation. AI systems get more accurate over time, with reinforcement learning helping them to become more adept at weeding out false alarms and spotting bigger issues.

Robotics

Robotics is another area where reinforcement learning shines. Programming a robot to move through a set layout is manageable, but what about when the space being navigated changes dynamically as people move around? A robot equipped with a reinforcement learning system can build a map in real time and update it whenever it notices a difference. And when robots are carrying out their allocated tasks, they can incrementally get faster at finishing them.

Advantages of reinforcement learning

Reinforcement learning offers a range of benefits that can drive significant improvements in system operations. Here are some to consider:

Continuous improvement: While this is the most obvious benefit, it’s also the most important. Using reinforcement learning, AI agents can learn from their actions and significantly increase the value of their outputs. And as reinforcement learning matures, the rate and breadth of improvement will usually increase.
Adaptability in dynamic environments: Reinforcement learning systems excel in settings where conditions change unpredictably, making them ideal for situations that would otherwise be non-viable for automation. And since optimization is crucial for industrial equipment, installing systems that can reconfigure themselves could help production facilities reduce the need for costly manual monitoring and adjustments.
Automation of complex processes: Equipped with reinforcement learning, AI agents can become capable of autonomously handling complex processes that would previously have required continuous human oversight. This allows professionals to focus on other and higher-level strategic tasks while the AI deals with day-to-day administrative operations, though keeping a human in the loop of operations is always a good idea.
Potential for innovation: By functioning through trial and error and lacking the habits that may inhibit people from finding novel approaches, AI agents using reinforcement learning can uncover fresh strategies across a wide range of industries.

Challenges of reinforcement learning

While reinforcement learning holds remarkable promise, it comes with a number of challenges that can make it tough to use. Below are some to watch out for.

High cost of computational resources

Reinforcement learning is extremely computationally demanding. Training and running the models can be very expensive, and not all businesses have the resources to accommodate the prospective costs. Scalable cloud solutions can help to mitigate this issue, making it easier to start small and scale up only when ROI has been demonstrated.

Slow improvement

Reinforcement learning's self-improvement cycle takes time. Numerous cycles are necessary for best results, and it’s important to factor this in when planning to integrate these models into your workflows. For the same reason, it’s best not to include reinforcement learning-powered models in production before they have been successfully trained and are already producing target results; there may be a risk of them making unpredictable decisions, wasting resources, or failing to meet target results.

Hard-to-design reward systems

A reinforcement learning model's success depends on aligning its reward mechanism with its objectives. If the feedback isn’t entirely in sync with the goals, the iterative process won’t move smoothly toward optimization and may even move in the wrong direction. To ensure you don’t encounter issues in this area, invest in reliable systems from trusted developers and get skilled people in place to oversee them.

Unstable training dynamics

Even when the overall trend of an AI agent is clearly positive, excess variability in output can lead to unacceptably inconsistent results. A system that performs extremely well 95% of the time and bizarrely poorly 5% of the time is still unfit for use in most situations. Consider using a staging environment to establish consistency (and possibly implementing rules to raise the importance of using broad patterns) to help address this issue.

Future trends in reinforcement learning

We’ve explored how reinforcement learning yields dynamic systems that can learn from experience, as well as its practical applications across industries, and the advantages and challenges of these adaptive systems. Let’s finish by looking at what this technology could achieve in the coming years.

Escalating processing complexity: Deep reinforcement learning offers a compelling blend of power and versatility. And as computing costs decrease and people come to better understand the power of the technology, we could see reinforcement learning used for evermore complex tasks, bolstering efficiency across the board.
Use in versatile AI solutions: So many AI technologies are highly compatible, and reinforcement learning can slot perfectly alongside them. It’s possible to blend reinforcement, supervised and unsupervised learning, for instance, leveraging the positives of each method, and the hybrid approach is likely to become very common.
Greater stability and speed: There’s immense scope for further refinement of reinforcement learning, and development will likely make the process faster and more stable. Businesses could consequently be able to use AI agents for evermore essential applications, knowing they can rely on them to perform consistently.
Wider industrial adoption: Reinforcement learning is already proving its worth in many sectors, and businesses in others (particularly at enterprise level) may increasingly embed reinforcement learning systems to help them manage repetitive tasks. In time, it will seem strange to use AI agents incapable of self-improvement.

Reinforcement learning represents a serious step forward in how AI systems function. While it isn’t without challenges, it’s still worth considering how well reinforcement learning could drive progress in your organization.

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