What Is Reinforcement Learning?

Reinforcement learning (RL) is a machine learning technique for training an agent to make optimal decisions by interacting with its environment and learning from experience. Through trial and error, the agent is guided by rewards or penalties, continually refining its strategy to maximize total rewards over time.

What are the Benefits of Reinforcement Learning?

Reinforcement learning is a valuable technique for developing optimized intelligent systems suited for complex tasks with high-dimensional state and action spaces, such as robotics, autonomous driving, scientific research, and game playing.

Real-Time Adaptability: Specialized decision-makers, such as an AI agent or robot, can continuously adapt to changing environments and learn from new experiences and real-time feedback, making them highly versatile.
Reduced Reliance on Labeled Data: Unlike supervised learning, reinforcement learning doesn’t require labeled training data. Instead, it learns through exploration, interacting directly with the environment.
Strong Generalization: Robots and software systems trained using reinforcement learning can generalize their knowledge and reasoning skills to new, unforeseen situations, to reinforce optimal performance in varied scenarios.
Specialized, Autonomous Decision-Making: Developers can craft custom reward functions aligned with specific enterprise or task objectives, shaping specialized agent behaviors for everything from resource management and software development to anomaly detection.

What are Applications of Reinforcement Learning?

Specialized AI Agents

Reinforcement learning is foundational for building specialized AI agents that autonomously reason, collaborate, and adapt within multi-agent systems. In financial services, for example, specialized fraud detection agents learn continuously from both simulated environments and real-time transactional data, enabling dynamic decision-making and rapid adaptation to new fraud patterns for effective risk mitigation.

Robotics

Reinforcement learning can be used in simulated environments to train and test robots, where they can safely learn through trial and error to improve skills such as control, path planning, and manipulation. This helps them develop sophisticated gross and fine motor skills needed for real-world automation tasks such as grasping objects, quadrupedal walking, and more.

Self-Driving Cars

Deep reinforcement learning—which integrates deep neural networks with reinforcement learning—has proven highly effective for developing autonomous vehicle software. Deep reinforcement learning excels in managing the continuous state spaces and high-dimensional environments present in driving scenarios. With real and synthetic sensor and image data used in a simulated model of the environment, deep reinforcement learning algorithms can learn optimal policies for driving behaviors like lane keeping, obstacle avoidance, and decision-making at intersections.

Industrial Control

Reinforcement learning can be used to teach industrial control systems to improve decision-making by allowing them to learn optimal control strategies through trial and error in simulated environments. For example, with a simulated production line, an RL-based controller can learn to adjust machine parameters to minimize downtime, reduce waste, and optimize throughput. Once the model is ready, it can be deployed in the real world.

Hyper-Personalization Across Industries

Adaptive AI systems are revolutionizing how industries tailor their offerings—whether it’s individualized promotions in retail, dynamic inventory routing in supply chains, or real-time video analytics for in-store optimization. Reinforcement learning underpins these innovations, allowing systems to continuously improve personalization and workflow decisions using direct feedback from customer interactions and operational outcomes.

Game Applications

Reinforcement learning can be used to develop strategies for complex games like chess by training agents to make optimal decisions through trial and error. The agent learns by interacting with the game environment, receiving rewards for positive outcomes (e.g., winning, capturing pieces) and penalties for negative ones (e.g., losing). Through self-play and balancing exploration with exploitation, the agent continuously improves its strategy, ultimately achieving high-level performance.

Core Components of Reinforcement Learning

Reinforcement learning is fundamentally based on the Markov decision process (MDP) framework, which is used to model sequential decision-making problems where outcomes are influenced by both randomness and the actions of an agent.

Key components include:

Agent	The learner or decision-maker taking action (e.g., an algorithm or model or software system)
Environment	The space in which the agent interacts with different variables
State	The current condition of the environment in which the agent exists
Action	The potential decision or step the agent makes to interact with the environment
Reward	The feedback (reward or punishment) the agent receives based on an action it takes

In reinforcement learning, an agent observes the current state, takes an action by following a policy, and the environment responds by providing a new state and a reward signal. The agent’s objective is to learn and adapt the policy that maximizes cumulative rewards over time, improving its decision-making through interaction with the environment, rather than explicit instruction.

How Does Reinforcement Learning Work in AI?

Unlike supervised learning, which relies on labeled datasets and direct feedback, reinforcement learning uses indirect feedback through a reward function that measures the quality of the agent’s actions.

Here’s a simple breakdown of how the process works:

Initialize: Define the environment (states, actions, rewards) and initialize the agent’s policy or value function (random or pretrained).
Action: Based on its current state, the agent chooses an action according to its decision-making policy. Actions can be discrete or continuous, based on whether the choice of possible actions is finite or infinite. For example, a simple game where the player can only move left or right uses discrete actions. On the contrary, a fraud detection system continually adjusts its reasoning and risk scores in real time, learning to discern true fraud alerts from false alarms.
Interact: The agent acts within the environment using the chosen action, leveraging necessary tools, if needed.
React: The environment transitions to a new state in response to the action. The environment responds with a new state and a reward, which indicates the consequence of the action.
Gather experience: The agent gathers experience samples—observes the rewards and state transitions, and uses this information to update its policy. This is called gathering trajectories. A trajectory is a state, reward, and action pair. The length of the trajectory and number of samples are hyperparameters that need to be defined by the user.
Learn: The agent updates its policy (or value function) based on the trajectories through an optimization process. This update is performed using RL algorithms such as model-based or model-free methods, depending on the specific goals of the task at hand.
Repeat: The process is repeated, allowing the agent to continuously learn and optimize its behavior through trial and error.

By following these steps and continually refining its decision-making policy through analysis of its actions and the rewards received, the RL agent becomes more adept at managing unforeseen challenges. This makes it more adaptable for real-world tasks that require specialization.

Reinforcement vs. Supervised vs. Unsupervised Learning

Supervised, unsupervised, and reinforcement learning are the three main approaches to machine learning that define how a model learns from data. Each learning technique is designed to solve distinct types of problems based on the nature of the data and feedback available.

The table below highlights their key differences:

Aspect	Supervised Learning	Unsupervised Learning	Reinforcement Learning
Main Idea	Learn from a labeled dataset to predict an output.	Learn from an unlabeled dataset to find patterns, structures, or relationships within the data.	Learn by interacting with the environment via feedback.
Type of Data	Labeled data where each data point has a corresponding correct output or “label.”	Unlabeled data where the data has no predefined outputs.	No predefined or labeled data. Can leverage ground truth, if available.
Goal	To map input data to known output labels to make accurate predictions on new, unseen data.	To find hidden patterns or groupings in data, such as clustering similar items or reducing data dimensionality.	To learn the optimal sequence of actions to achieve a specific long-term goal.
Common Problems	Classification (e.g., is this email spam or not?) and regression (e.g., predicting house prices based on features).	Clustering (e.g., customer segmentation) and association (e.g., market basket analysis, “people who bought X also bought Y”).	Sequential decision-making (e.g., playing a video game, reasoning through complex problems, controlling a robot arm, or training a self-driving car).

How Do You Set Up Environments for Reinforcement Learning?

In reinforcement learning, the environment is the world the agent operates in. It defines the rules, constraints, and outcomes that determine how the agent learns. Setting up an environment involves specifying the states, actions, and rewards, along with verifiers and tools—which validate whether the agent is learning correctly and provide additional capabilities to solve tasks more effectively.

Together, these elements form the training playground where the agent interacts, experiments, and improves through trial and error.

The Role of a Gym

To simplify this process, the AI community leverages the concept of a gym, which standardizes how environments are created and managed. Rather than building a custom simulator each time, practitioners can access a library of ready-made environments ranging from simple control problems to more complex scenarios like games or robotic simulations.

Think of a gym as the training ground for reinforcement learning agents:

A consistent interface for states, actions, and rewards
Built-in tools the agent can use to complete tasks
Verifiers to measure whether outcomes are correct, safe, or aligned with goals
Standardization that ensures results are reproducible and comparable

Example: Specializing an AI agent using a gym

Consider a general-purpose model powering an AI helpdesk agent. By placing it into a simulated customer-support environment, the agent can gain specialized skills:

The environment defines rules such as response time limits, ticket categories, and escalation policies.
The agent tries different actions—routing a ticket, drafting a response, or escalating to a supervisor.
Rewards signal outcomes: positive if the issue is resolved quickly and accurately, negative if the customer remains dissatisfied.
Tools might include access to a knowledge base or CRM system.
Verifiers check compliance: Was the answer factually correct? Did it follow brand tone and legal guidelines?

Through repeated cycles of action → reward → verification, the helpdesk agent sharpens its responses, adapts to nuanced cases, and develops specialized expertise—transforming from a general model into a task-ready AI agent that can operate effectively in production.

What are the Types of Reinforcement Learning Algorithms?

Model-Based Methods

Model-based RL systems are particularly effective in well-defined or stable environments, or when real-world testing is costly or unsafe. The agent first builds a representation of the environment by using a transition model and reward model:

With a transition model, an agent experiences a real environment (or has access to one) and predicts the next state based on the current state and action.
A reward model then estimates a granted reward associated with specific state-action pairs.

The agent can then simulate future interactions with the environment without relying entirely on trial and error. Examples are Monte Carlo Tree Search (MCTS) used in AlphaGo and AlphaZero, and Dyna-Q (a hybrid of model-based and model-free).

World models support reinforcement learning by enabling agents with a simulation environment to practice and predict the outcomes of their actions, significantly improving sample efficiency and reducing the need for costly real-world experimentation.

Reinforcement Learning From Human Feedback (RLHF): This method incorporates human input into the learning process, allowing the agent to learn from both environmental rewards and human feedback. Humans provide evaluations or corrections on the agent’s actions, which are then used to adjust the agent’s behavior, making it more aligned with human preferences and expectations. This approach is particularly useful in tasks where defining a clear reward function is challenging.

Model-Free Methods

An agent learns to make decisions based solely on direct interactions with the environment, without building or relying on a model of the environment. Essentially, the agent doesn’t try to predict future states or rewards explicitly, but learns from the feedback it gets from the environment after taking actions through trial and error.

Policy Gradient Methods: These methods directly teach the agent to learn a policy function that specifies which action to take based on the current state. Examples include REINFORCE (Monte Carlo Policy Gradient), Deterministic Policy Gradient (DPG), etc.
Value-Based Methods: These methods teach an agent to learn optimal actions by updating a value function (like the state value function 𝑉(𝑠) or the action-value function 𝑄(𝑠,𝑎)) that estimates how beneficial it is for the agent to be in a certain state or take a certain action. Q values are the expected rewards of taking an action at a specific state. These methods don’t explicitly model the policy, but derive the optimal policy from the value function. Examples include Q-learning, Deep Q-Networks (DQN), SARSA, Double Q-learning, etc. Applications of Q-learning include Atari games, algorithmic trading, and robot navigation and control.
Actor-Critic Methods: This combines the strengths of both policy-based and value-based approaches. The “actor” is responsible for selecting actions based on the current policy, while the “critic” evaluates the quality of those actions by estimating the value function. The actor updates its policy in the direction suggested by the critic, aiming to maximize the expected cumulative reward. Examples include A2C, A3C, DDPG, TD3, PPO, TRPO, SAC, and others. Actor-critic methods are used in applications that include robotics, game play, and resource management.

Next Steps

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