AI Reinforcement Learning: A Complete Guide (2026)

AI agents are now capable of handling complex tasks that once required regular human input. But capability doesn’t always equal reliability. When conditions change, even a well-built agent can start to drift. To solve this, teams need systems that can learn from outcomes in real environments. This is where AI reinforcement learning (RL) can help.

Reinforcement learning is a training method where AI agents learn from trial and error in real-world situations. Whenever they take an action, they receive a ‘reward’ or a ‘penalty’, depending on the outcome, which will improve their decision-making capabilities over time. This feedback loop is the engine behind many modern generative AI and LLM (large language model) applications.

This guide will explain how reinforcement learning works, what main algorithms are available to you, and how you can apply the method to build high-performing artificial intelligence models and more reliable, consistent experiences for your colleagues and customers.

• What is AI reinforcement learning?
• How does AI reinforcement learning work?
• Key AI reinforcement learning algorithms explained
• Reinforcement learning vs. other machine learning approaches
• Benefits and limitations of AI reinforcement learning
• Real-world applications of AI reinforcement learning
• Reinforcement learning with Salesforce
• FAQs

Reinforcement learning is an advanced approach to AI training that uses interactions to learn and improve. Rather than an AI program being told the right answers immediately (as with supervised learning), RL allows it to discover which actions yield the highest rewards through trial and error when faced with unfamiliar environments or situations.

There are five core components that form the basis of any RL strategy:

• Agent: This is the AI entity or program that will be making the decisions. It’s the ‘learner’ that tries to navigate a pathway to its goal.
• Environment: This is the world or context within which the RL agent ‘lives’, such as a road network for a self-driving car or a live stock market for an AI trading tool.
• State: The state is a snapshot of the environment. It gives the agent an understanding of where things stand at a given time.
• Action: An action is the decision the agent will make within that moment based on the available information and the current state.
• Reward: This is the feedback signal. Positive rewards reinforce good decisions (for example, a customer support agent providing the correct solution to a problem), while negative rewards (penalties) discourage poor ones.

Simply put, the agent takes an action within its environment based on the current state. It then receives feedback (a reward or penalty) and updates its behaviour. Over many cycles, this leads to better decision-making and more accurate outcomes.

Reinforcement learning is a dynamic process that allows an AI agent to discover the best ways to behave through continuous interaction.

Rather than following a rigid script, the agent learns a policy. Think of this as a detailed approach to decision-making that evolves as the agent gathers more feedback from its environment. Over time, this increases the likelihood of actions that lead to good outcomes.

Reinforcement learning is powerful because it mirrors the way humans learn through experience: We try something out, see whether it works, then refine our approach based on the outcome. This makes the process extremely intuitive.

But learning through trial and error does raise a question. Should the agent stick with what’s already worked, or test out new options? This is the exploration vs. exploitation dilemma:

• Exploration: The AI tries new, unfamiliar actions to see if they lead to better rewards.
• Exploitation: The AI sticks to known actions that have worked in the past to secure a guaranteed win.

Leaning on exploitation too heavily can make an agent static and fragile. It will keep doing what once worked, even when the environment changes. On the flipside, constant exploration can lead to more damaging, less predictable outcomes in the short term. Usually, the goal of effective RL is to balance both so agents can operate reliably while continuously improving.

Three key concepts guide how an RL agent makes decisions and behaves over time: policies, rewards, and discounting.

A policy is the AI’s internal ‘rulebook’. It dictates how the agent should act in any given state. These are flexible sets of instructions that are designed to be altered and adapted over time based on event feedback. There are two forms of AI learned policies to know about:

• Deterministic: These are the core principles and behaviours that are unlikely to change over time. The same situation leads to the same action and the same outcome.
• Stochastic: This is when the AI uses probability to choose between different actions when faced with new states, allowing for more fluid behaviour.

Stochastic policies can occasionally extend to deterministic policies if an AI takes initiative and discovers a decision that’s a potentially superior choice to the established action.

Rewards are the signals that tell the AI model if it’s on the right track and working toward the desired outcome.

• Positive, cumulative rewards (reinforcement) encourage the AI to repeat successful behaviours (moving an action from stochastic to deterministic).
• Negative rewards (penalties) teach AI to avoid mistakes that move it further from its goal, encouraging continued exploration.

The goal here is to shape behaviour over time to encourage consistent progress in the right direction.

The discount factor controls how much the AI values future reward signals as opposed to immediate ones (often represented by gamma). They are split into low discount factors (a focus on quick wins) and high discount factors (long-term success).

For example, a stock trading agent focused on short-term trades for quick profit would have a lower discount factor than one optimised for long-term, steady investment strategies.

Reinforcement learning isn’t one single technique. There are various approaches tailored to specific environments, each with a different way of interpreting data and deciding what to do next. Some will prioritise the ‘value’ of the current state to determine their next decision, whereas others will focus predominantly on the policy of the agent. Others will attempt to offer a hybrid of both approaches.

There is no single ‘best’ RL algorithm. In 2026, the choice depends entirely on the problem; simple decision-making tasks might use value-based methods (particularly in the case of binary decision-making), while complex, continuous robotics or LLM tuning may turn to more advanced policy optimisation.

What follows is a list of some of the most prevalent deep reinforcement learning algorithm models that businesses can use for their AI programs.

Value-based methods try to predict the total reward an agent can expect from a given state (or from taking an action within a given state). In essence, it builds a value map of the environment, then chooses the actions it thinks will maximise expected returns. Here are some examples:

• Q-learning learns a ‘Q-value’ for each state-action pair, then represents it in a table to determine the value function of taking a particular action in a current state. It’s an off-policy method, meaning the agent can learn an optimal strategy independently of the behaviour policy the agent is currently using to explore.
• SARSA (state-action-reward-state-action) is an on-policy reinforcement learning algorithm. It updates Q-values dynamically based on the next action the agent takes under its current policy, rather than assuming it will take the next best action independently. Simply put, it learns the value of the policy the agent is currently following.

These methods are a good fit for simpler decision spaces where actions are easy to number and compare.

Policy-gradient approaches skip the value map entirely, instead learning the policies directly and determining which action to take to maximise long-term rewards.

A common example here is REINFORCE , which updates the policy using gradient ascent (adjusting the model’s parameters step by step to increase the likelihood of favourable actions) to produce better outcomes over time.

These methods tend to be useful when the best action is based on probability, rather than being fixed, or when the action space is too large for a simple value table.

This is a hybrid method that combines the previous two ideas; the actor represents the policy-gradient approach, and the critic represents the value-based component.

There are two forms of actor-critic methods:

• A2C (Advantage Actor-Critic): With this approach, the critic (values) informs the actor (policy gradient) how much better an action was than the average.
• A3C (Asynchronous Advantage Actor-Critic): This is a more advanced version of A2C, where multiple agents learn in parallel across different versions of the environment.

This algorithm, also known as Trust Region Policy Optimisation (TRPO) , is designed for high-stakes environments where stability is key. In the exploration vs. exploitation dilemma, it falls firmly in the latter camp.

TRPO constrains how much the policy can change in a single update, reducing the chance that it breaks the model’s performance. This is especially important for complex industrial systems and high-stakes settings where learning faster isn’t worth the risk of the agent making unpredictable decisions.

Reinforcement learning isn’t the only method AI systems can use to refine their functionality and improve over time.

There are two other core machine learning (ML) approaches, each designed to fit different types of problems and operations. These are supervised learning and unsupervised learning. Let’s see how they stack up against and compare with reinforcement learning.

Supervised learning is perhaps the most stable approach to machine learning, as the potential decisions an AI program can make have already been determined. The system is trained on labelled data, with the overall goal being prediction quality and accuracy.

Some real-life use cases of supervised learning in action include:

• Fraud detection: Models are trained on past transactions that have been labelled as fraudulent or legitimate. Whenever the model examines a new transaction, it can use clues from previous iterations to determine the likelihood of fraud.
• Customer support automation: Agents will be trained to understand a broad set of ticket types based on factors associated with the complaint. They can then predict which category new queries fall into and respond with the appropriate action.
• Email filtering: Agents are often trained to recognise the contents of spam or phishing emails. Then, when a new email that shares similar features arrives, they can immediately disregard or quarantine the email.

Reinforcement learning, on the other hand, doesn’t rely on labelled data to make informed decisions. Instead, it interacts directly with its environment, with feedback coming in the form of rewards or penalties, depending on how effective the chosen action is. Use cases include:

• Personalised recommendations: Agents will use past customer behaviour to suggest potential products the customer might be interested in. The immediate reward will arrive if the customer subsequently chooses to follow through with the purchase.
• Marketing optimisation: Agents will explore various ad placements to determine where they’re most successful. This will then have an impact on budget allocation.
• Dynamic pricing: Agents will examine various factors within their environment, including demand, competition and customer behaviour, to try and arrive at optimal pricing that will drive the most revenue.

Fisher & Paykel is a textbook example of reinforcement learning in action. The company used Salesforce AI and applications, such as Data 360 and Agentforce Marketing, to create a single source of truth for customer data. From there, they were able to create hyper-personalised communication through reinforcement learning to deliver unique recommendations, resulting in a 40% increase in product views.

Unsupervised learning is the process of finding hidden structures or patterns within unlabeled data. There’s no concept of ‘right’ vs. ‘wrong’ outcomes. Instead, the system looks for similarities, clusters or relationships. It doesn’t make decisions or take direct actions.

Common use cases include:

• Market research: AI will identify trends in large datasets associated with a business’s competitors to identify potential strengths and limitations.
• Anomaly detection: An AI machine will spot unusual activity within transactions or system logs.
• Product clustering: For marketing teams, an AI program could be used to group together similar products or services within their catalogue that might appeal to a certain subset or demographic.

In contrast, reinforcement learning is action-driven. The AI is geared towards making decisions that will determine future outcomes and, through feedback, understanding whether those decisions were the correct path to take. Over time, the AI will refine these correct pathways based on the rewards it has received.

Applications like recommendation engines (think Netflix and Amazon and their ‘you may also like’ features), resource allocation, automated trading strategies, and customer experience management are all examples of reinforcement learning in action.

AI reinforcement learning is most useful when you want AI to do more than just make predictions. It helps agents make better decisions over time, which is why it shows up in everything from self-driving cars and generative AI to advanced robotics.

At the same time, it does come with trade-offs. The more autonomy you give an agent, the more difficult it becomes to design, monitor, and control it. Let’s take a look at some of the strengths and drawbacks of this approach to get the complete picture.

Reinforcement learning’s main advantage is that it’s able to learn and refine its practices autonomously over time. This gives it a distinct advantage over models that only rely on static data, as reinforcement learning machines can adapt to changing market conditions and react to unexpected events and situations.

Other benefits include:

• Complex problem-solving capabilities: Reinforcement learning is the best approach for areas like robotics that require complex, multi-layered decision-making. As robots are designed to mimic human behaviour, they learn best through trial and error, which is exactly where the reinforcement learning approach excels.
• Novel solutions: When a reinforcement learning program is given a wide scope to try new solutions, it can develop ideas that human agents may overlook.
• Built for long-term success: Because it’s designed to react to changing conditions, reinforcement learning is often geared towards long-term stability and success. This is why platforms like Salesforce AI utilise reinforcement learning to optimise customer journeys that change based on both current and past behaviours.

Despite its strengths, reinforcement learning’s complexity means it’s often one of the trickier ML approaches to implement successfully. Here are some limitations to consider:

• High computational costs: Training complex reinforcement learning agents, particularly with the intention of eventually moving to an agentic AI infrastructure, requires a large amount of electricity and computational resources.
• Reward design challenges: If the reward is too narrow or poorly defined, the agent may use shortcuts to ‘score a quick win’ without actually doing what you want. For example, if an LLM is asked to provide a response with a minimum word count, it may populate the response with filler words to get the desired outcome.
• Safety and explainability: If a reinforcement learning agent is encouraged to try new solutions in order to solve a problem, it could arrive at solutions that are unsafe or unethical. There are also concerns about explainability and trust; engineers must be able to explain why an AI model chose a specific decision.

While reinforcement learning can be the key to high-performing AI algorithms, it’s important to pair it with clear design and strict guardrails so learning doesn’t come at the expense of outcomes, trust and safety.

Reinforcement learning is already utilised across a number of industries. It’s the preferred choice in any situation where AI needs to make decisions in dynamic environments. Here are some of the top RL use cases today:

The most visible modern application of RL is within generative AI models like GPT-4, Claude or Salesforce xGen-small.

Fundamentally, LLMs still work by predicting what the most likely next word is in order to form coherent sentences that a human can understand. However, many have moved from simple predictions to Reinforcement Learning from Human Feedback (RLHF). This adds a human feedback loop into the process that helps align outputs with what people consider helpful and safe.

Reinforcement learning is hugely important for the training of robots in many industries. Robots are trained to emulate human movements through trial and error, receiving positive feedback when a movement is successful. Over time, the robot can then accumulate a bank of ‘desired actions’ that it understands will lead to the ideal outcome.

RL powers feats of engineering, such as robotic arms in manufacturing, self-driving cars, and warehouse drones, which are capable of precise movements that drive productivity at scale.

Reinforcement learning helps to identify optimal treatment paths over time. AI systems can evaluate how patients respond to treatments and suggest adjustments based on patient outcomes.

It also supports a more personalised care approach. Systems learn which treatments work best for specific patient profiles, helping them create tailored care plans that continuously adapt and evolve as new data becomes available.

Barwon Health’s revolutionary approach to holistic healthcare is an excellent example of reinforcement learning in action. Using Salesforce’s Customer 360 platform, the company was able to bring together input from clinicians and specialists and integrate the feedback, all supported by real-time data, to create truly optimised patient care plans.

Reinforcement learning also helps to optimise logistics, inventory and scheduling decisions by examining historical data patterns and trends. Systems can learn how to reduce delays and costs while adapting to demand changes and supply chain disruptions, improving efficiency across complex operations.