Reinforcement learning (RL) is a branch of machine learning focused on decision making. Unlike supervised or unsupervised learning, reinforcement learning trains algorithms to select actions within a defined environment by learning from outcomes. Through a trial and error process, the system evaluates the results of its actions, receives feedback as rewards or penalties, and updates its behavior to improve future decisions.
In simple terms, reinforcement learning allows an artificial intelligence system to learn autonomously by maximizing cumulative rewards over time. This approach is especially effective for sequential decision making in dynamic environments, making it well suited for applications such as robotics, game playing, and real-time control systems.
Key takeaways
- Reinforcement learning is a branch of machine learning focused on sequential decision making, where agents learn optimal behavior through interaction with an environment and feedback in the form of rewards.
- Supervised learning, unsupervised learning, and reinforcement learning solve different problems, with supervised learning relying on labeled data, unsupervised learning uncovering hidden patterns in unlabeled or unstructured data, and reinforcement learning optimizing decisions over time.
- Reinforcement learning algorithms operate through a feedback loop involving agents, actions, states, rewards, and environments, enabling learning through trial and error and long-term reward optimization.
- Modern reinforcement learning methods fall into four main categories—value-based, policy-based, model-based, and actor–critic—each offering different tradeoffs in efficiency, stability, and adaptability.
- Scalable data platforms like MongoDB Atlas are critical for reinforcement learning and deep reinforcement learning workloads because they support large datasets, real-time data processing, flexible schemas, and secure, distributed training environments.
Table of contents
- Where reinforcement learning fits in modern AI systems
- What's the difference between supervised learning, unsupervised learning, and reinforcement learning?
- How do reinforcement learning algorithms work?
- The four primary categories of reinforcement learning algorithms
- What's the difference between generative AI and reinforcement learning?
- Why MongoDB Atlas is ideal for reinforcement learning tasks
- Reinforcement learning: A foundation for adaptive, decision-driven AI
- Related reading
- FAQs
Where reinforcement learning fits in modern AI systems
Artificial intelligence (AI) is a broad field focused on building systems that can perform tasks typically associated with human intelligence, such as perception, reasoning, and decision-making. Within AI, machine learning refers to techniques that allow systems to improve their performance through experience rather than explicit programming.
Many machine learning approaches focus on recognizing patterns in data. Large language models (LLMs), for example, are trained to predict and generate text based on statistical relationships learned from large corpora. While these models can be adapted for tasks like ranking or prioritization, they are generative systems by design. They do not inherently optimize decisions against a defined objective or learn from the long-term consequences of their actions. When used as rerankers, they approximate judgment rather than systematically improving decision quality over time.
Reinforcement learning addresses a different class of machine learning problems centered on sequential decision making. Rather than predicting outputs, reinforcement learning trains an autonomous agent to take actions within an environment, receive feedback in the form of rewards or penalties, and update its behavior over time. This feedback loop enables the agent to optimize decisions based on future rewards, making reinforcement learning especially effective in dynamic environments where actions influence future states and outcomes.
Some modern ranking systems are designed to combine instruction-following with evaluative decision-making. For example, rerankers developed by Voyage AI are built to follow instructions while remaining decision-oriented rather than generative, preserving the consistency, reproducibility, and performance characteristics required for large-scale optimization.
Within machine learning, most techniques fall into one of three learning models: supervised learning, unsupervised learning, and reinforcement learning. Each approach represents a different way of learning from data, feedback, and interaction. Understanding how reinforcement learning fits alongside these methods helps explain why it remains foundational for building reliable, goal-driven decision-making systems in modern AI.
What's the difference between supervised learning, unsupervised learning, and reinforcement learning?
In machine learning, algorithms learn in different ways depending on the type of data available and the problem being solved. The three primary learning approaches are:
Supervised learning
Unsupervised learning
Reinforcement learning
Each method uses a distinct training process and is suited to different kinds of tasks, from prediction and pattern discovery to sequential decision making.
Supervised learning
Supervised learning is a machine learning method where an algorithm learns to make predictions or descriptions based off of labeled data (for example, tasks such as named entity recognition). This type of learning is supervised because the training process is similar to a teacher's, ensuring the algorithm is corrected and guided toward the right outcome during its training phase.
Use cases include:
Weather predictions.
Sales forecasts.
Stock price analysis.
Marketing forecasts.
Population growth predictions.
Cancer detection.
ID fraud detection.
Customer retention.
Diagnostic predictions.
Unsupervised learning
Unsupervised learning works with unlabeled data, meaning the algorithm is not given predefined outcomes or target values. Instead, it analyzes the input data to discover hidden patterns, relationships, or groupings on its own. This approach is especially useful when the structure of the data is unknown or when labeling data is impractical. Because unsupervised learning works without labeled outcomes, it is commonly applied to unstructured data, where meaning must be inferred from raw text, logs, or documents rather than predefined schemas.
Use cases include:
Customer segmentation.
Churn analysis.
Recommendation systems.
Target marketing.
Big data visualization.
Reinforcement learning
Reinforcement learning focuses on teaching algorithms to make optimal decisions through interaction with an environment. Rather than learning from labeled examples, a reinforcement learning agent learns through trial and error, taking actions, observing outcomes, and receiving feedback in the form of rewards or penalties. Over time, the agent learns a strategy, or optimal policy, that maximizes cumulative rewards.
A key challenge in reinforcement learning is balancing exploration and exploitation. The system must explore new actions to discover better strategies while also exploiting known actions that lead to desirable outcomes. This makes reinforcement learning particularly effective for sequential decision making in dynamic environments.
Some notable real-world applications include:
Video game AI: This use case involves the design of advanced AI agents that can play and adapt to video game scenarios, showcasing the ability to learn complex strategies and make quick decisions.
Robotics for human tasks: In this application, reinforcement learning is applied to robots, training them to perform tasks typically done by humans, which involves learning from interactions in real-time or in a simulated environment.
Structured environment robotics: This approach involves implementing reinforcement learning in structured environments like automotive plants, where robots can learn and adapt to pre-programmed behaviors, enhancing efficiency, precision, and productivity.
Mastering chess: Developing AI that can learn and excel at chess is a classic example of reinforcement learning's strength in decision-making and strategy development.
Real-time decision-making: With this scenario, reinforcement learning allows for quick and efficient real-time decisions that are crucial, such as in dynamic and unpredictable environments (like traffic, for example).
Learning tasks: This broader use case illustrates how reinforcement learning helps systems learn and improve at tasks through ongoing practice and adaptation.
Together, supervised learning, unsupervised learning, and reinforcement learning form the foundation of modern machine learning. Understanding how these approaches differ helps clarify which techniques are best suited for prediction, pattern discovery, or long-term decision optimization in artificial intelligence systems.
How do reinforcement learning algorithms work?
Reinforcement learning algorithms learn through interaction with an environment. At their core, they are designed to train a learning agent to make decisions, observe outcomes, and improve behavior over time using feedback. This process is driven by a small set of foundational components that define how reinforcement learning systems operate. These include:
Agent: Also known as the learner or decision-maker
Environment: The setting or context where the agent operates
Observation: The agent's immediate, perceptual input from the environment, such as visual data or sensor readings
Actions: What the agent can do
State: The current situation of the agent
Rewards: Feedback from the environment in response to the agent's actions
The agent receives an observation (e.g., pixels on a screen, sensor readings) from the environment, which represents the state (the underlying situation) at that moment. The agent's goal is to infer the state from its observations and choose the best action.
As the game progresses, the state of the game evolves according to the player’s decisions. Successful actions earn points or bonuses, which function as rewards. Over time, the player learns through trial and error, identifying strategies that lead to higher rewards and better outcomes. Reinforcement learning algorithms follow the same principle, gradually learning an optimal policy that maximizes cumulative rewards.
Here's the cast of this interactive learning game, aligned with the essential elements:
Agent: The main character, learning and making decisions
Environment: The game world, with its unique challenges and opportunities
Observation: The real-time sensory input or data the agent sees and experiences in the game, like visual scenes, sounds, or game scores
Actions: Moves the agent can make, like jumping, running, or solving puzzles
State: The current scene or level the agent is navigating
Rewards: Points or bonuses given for successful actions
In practice, reinforcement learning algorithms enable systems to adapt, learn, and improve decision making in dynamic environments. By continuously interacting with their surroundings and responding to feedback, these algorithms are well suited for real-world applications where conditions change and decisions must account for future consequences.
The four primary categories of reinforcement learning algorithms
Once the basic reinforcement learning framework is understood, the next step is to examine the major categories of reinforcement learning algorithms. These algorithms define how an agent evaluates actions, learns from experience, and optimizes decisions over time. In practice, most reinforcement learning methods fall into four primary categories: value-based, policy-based, model-based, and actor–critic algorithms. Each approach is suited to different types of reinforcement learning problems and environments.
Before exploring these categories, it’s important to understand the Markov Decision Process (MDP), which provides the mathematical foundation for most reinforcement learning algorithms. An MDP describes an environment in terms of states, actions, and rewards, and is based on the assumption that the future state depends only on the current state and the action taken. This framework underpins how reinforcement learning agents interact with their environment and learn optimal behavior.
Value-based algorithms
Value-based reinforcement learning algorithms focus on learning a value function, which estimates how good a particular state or action is in terms of expected future rewards. The agent uses this value function to select actions that maximize long-term returns. Rather than learning a policy directly, the agent derives a policy by choosing actions with the highest estimated value.
Common examples of value-based algorithms include Q-learning and Deep Q-Networks (DQN). These methods are often used in environments with well-defined states and actions, such as games or financial simulations. Typical applications include stock trading strategies and video game AI, where the goal is to maximize cumulative rewards like profit or score.
Policy-based algorithms
Policy-based reinforcement learning algorithms take a different approach by learning the optimal policy directly. Instead of estimating action values, these methods learn a mapping from states to actions, allowing the agent to decide what to do in each situation without relying on a value function.
Policy-based methods are particularly effective in environments with continuous action spaces or complex dynamics. Examples include policy gradient methods, REINFORCE, and Proximal Policy Optimization (PPO). These algorithms are commonly applied in robotics, autonomous driving, and personalized healthcare, where agents must learn nuanced behaviors and adapt to changing conditions.
Model-based algorithms
Model-based reinforcement learning algorithms explicitly learn or use a model of the environment. This model predicts how the environment will respond to different actions, allowing the agent to plan ahead and evaluate potential outcomes before acting. By simulating future states and rewards, model-based methods can be more sample-efficient than model-free approaches.
An example includes Dyna-Q. Model-based algorithms are often used in autonomous systems and energy management, such as predicting vehicle maneuvers or optimizing smart grid operations. However, their effectiveness depends heavily on the accuracy of the environment model, which can be difficult to achieve in complex real-world environments.
Actor–critic algorithms
Actor–critic algorithms combine elements of both value-based and policy-based reinforcement learning. The actor is responsible for selecting actions, while the critic evaluates those actions using a value function. This separation allows the agent to learn more efficiently and with greater stability than using either approach alone.
Popular actor–critic methods include Advantage Actor–Critic (A2C) and Asynchronous Advantage Actor–Critic (A3C). These algorithms are widely used in real-time strategy games and supply chain optimization, where agents must make fast, high-impact decisions while accounting for long-term outcomes.
Together, these four categories form the backbone of modern reinforcement learning algorithms. Understanding how they differ helps clarify which methods are best suited for specific environments, constraints, and decision-making goals.
What's the difference between generative AI and reinforcement learning?
Generative AI and reinforcement learning are both important approaches within artificial intelligence, but they are designed to solve different types of problems and operate in fundamentally different ways.
Generative AI
Generative AI focuses on creating new content. These systems learn patterns from large datasets and use those patterns to generate outputs such as text, images, audio, or code. The goal of generative AI is synthesis. Producing outputs that resemble the training data while introducing variation. Examples include large language models, image generation systems, and music generation tools.
Reinforcement learning
Reinforcement learning, by contrast, is focused on sequential decision making. Instead of generating content, reinforcement learning trains an algorithm to choose actions within an environment. A reinforcement learning agent learns through trial and error, receiving rewards or penalties based on its actions and improving its behavior over time. The objective is to learn an optimal policy that maximizes cumulative rewards.
In summary, generative AI is primarily concerned with content generation based on learned patterns, while reinforcement learning is concerned with learning how to act optimally over time through interaction, feedback, and long-term reward optimization.
Why MongoDB Atlas is ideal for reinforcement learning tasks
MongoDB Atlas provides a robust and flexible infrastructure, making it an excellent choice for reinforcement learning tasks.
Here are 10 top reasons why MongoDB Atlas is well-suited for reinforcement learning:
1. Scalable storage for large datasets
Reinforcement learning often requires handling large amounts of data, especially when training deep reinforcement learning models. MongoDB Atlas can easily scale to accommodate large datasets, ensuring efficient storage and retrieval.
2. High performance and low latency
MongoDB Atlas is designed to deliver high performance with low latency, which is crucial for real-time decision-making tasks in reinforcement learning. This ensures that RL models can quickly access and process data.
3. Flexible data model
MongoDB utilizes a document-oriented data model that allows for flexible and dynamic schemas, making it easy to store complex data structures such as state representations, reward functions, and policy parameters used in RL.
4. Global distribution
MongoDB Atlas supports global data distribution, which is beneficial for reinforcement learning applications that require data collection and processing across multiple geographical locations.
5. Advanced security
Security is paramount in any AI application. MongoDB Atlas provides advanced security features, including encryption at rest and in transit, role-based access control, and network isolation, ensuring that your RL models and data remain secure.
6. Integrated data tools
MongoDB Atlas integrates seamlessly with various tools and services such as MongoDB Charts for data visualization and Atlas App Services for real-time data synchronization, which can be valuable for monitoring and analyzing RL training processes.
7. Automated backups and recovery
Automated backups with point-in-time recovery ensure that you can easily recover from any data loss, which is critical when experimenting with different RL algorithms and models.
8. Serverless options
MongoDB Atlas offers serverless instances that automatically scale based on workload, providing a cost-effective solution for RL experiments that have variable computational demands.
10. Comprehensive monitoring and analytics
MongoDB Atlas includes comprehensive monitoring and analytics tools, allowing you to track the performance and resource utilization of your RL models, making it easier to optimize and troubleshoot.
Reinforcement learning is a groundbreaking force in AI, dramatically transforming how machines learn and decide. By brilliantly adapting to complex environments like robotics and gaming, it marks a thrilling leap forward in artificial intelligence, opening doors to endless possibilities and smarter, more intuitive technology.
Reinforcement learning: A foundation for adaptive, decision-driven AI
Reinforcement learning represents a core approach to building AI systems that can learn through experience, adapt to changing conditions, and make decisions that improve over time. By focusing on interaction, feedback, and long-term reward optimization, reinforcement learning addresses a class of problems that prediction-based methods alone cannot solve.
Understanding how reinforcement learning differs from supervised learning, unsupervised learning, and generative AI helps clarify when it is the right tool. From value-based and policy-based methods to actor–critic and deep reinforcement learning approaches, RL provides a flexible framework for sequential decision making in dynamic environments.
As reinforcement learning systems move from research into real-world applications, scalable and reliable data infrastructure becomes essential. Supporting large volumes of structured and unstructured data, real-time feedback loops, and evolving state information is critical to turning reinforcement learning models into production-ready systems that can operate safely, efficiently, and at scale.
Related reading
What are large language models (LLMs)?
The case against LLMs as rerankers
Voyage AI: Best-in-class embedding models and rerankers
What is named entity recognition (NER)?