Reinforcement learning
Reinforcement learning (RL) is a type of machine learning where an AI system learns by trial and error, guided by rewards and penalties. Instead of being trained on a fixed dataset of examples, an RL algorithm interacts with an environment, takes actions, observes results, and adjusts its behavior to achieve the best possible outcome. Over time, the system develops a strategy, or “policy,” that maximizes its cumulative reward.
This approach mirrors how humans and animals learn from experience. A toddler learns to stack blocks by trying different arrangements, noticing which ones topple and which ones hold. A reinforcement learning agent does something similar—testing actions, measuring results, and gradually improving its choices based on what works.
How reinforcement learning works
RL includes several key elements:
- Agent: The autonomous decision-maker that takes actions.
- Environment: Everything the agent interacts with.
- Actions: The set of choices available to the agent.
- Rewards: Feedback signals that tell the agent whether the outcome was good or bad.
- State: The environment at a point in time
The learning process is iterative. Reinforcement learning works in cycles: the agent looks at the current state of its environment, does something, and receives a reward (positive or negative). The agent then updates its internal model of which actions are most valuable in each situation. Over many iterations, it learns a policy. A policy is essentially a map from situations to actions that are likely to yield the highest total reward.
Exploration is an important part of reinforcement learning. At first, the agent must try many different actions, even ones that seem suboptimal, to learn about their consequences. Later, it shifts toward exploitation, relying more on the actions that have historically worked well. Finding the right mix of trying new actions and relying on proven ones is key to making RL work effectively.
Business applications of reinforcement learning
RL is more than a theory and powers real systems today, including:
- Robotics: Teaching robots to walk, climb stairs, or handle delicate objects.
- Autonomous vehicles: Helping cars learn to navigate safely and efficiently.
- Operations research: Optimizing delivery routes, warehouse picking, or inventory levels.
- Finance: Building adaptive trading strategies that respond to market conditions.
- Healthcare: Personalizing treatment schedules based on patient response.
Reinforcement learning and customer experience
In the world of customer service, RL can power adaptive agentic AI that gets better over time. For instance, an AI chatbot could experiment with different conversation flows and learn which ones lead to faster resolution or higher customer satisfaction. Over thousands of interactions, the system would discover the best sequence of actions—such as when to escalate to a human agent or when to offer a self-service link.
This approach can also personalize interactions. Reinforcement learning can tailor recommendations or solutions based on customer behavior, aiming to maximize satisfaction or loyalty as the “reward.” Of course, when AI is continuously learning, teams need visibility into what it’s doing and why. Strong AI observability practices make it possible to monitor performance and ensure the system’s decisions stay aligned with business goals.
Reinforcement learning is a powerful way for AI to learn by doing. Instead of following fixed rules, RL systems improve through testing and feedback. The result is smarter, more adaptive decision-making.
