Reinforcement Learning Pattern

The Reinforcement Learning Pattern is a type of machine learning where an algorithm learns how to make decisions by interacting with an environment. In this pattern, an agent learns to perform actions in a dynamic environment to maximise a cumulative reward over time. Unlike supervised learning, where the model learns from labelled examples, reinforcement learning involves learning through trial and error by receiving feedback in the form of rewards or penalties based on its actions.

The agent learns to make better decisions over time by exploring different actions and observing their outcomes. Through continuous interactions, the agent develops a strategy or policy that leads to the most favourable outcomes. This pattern has been used successfully in various applications, including game playing, robotics, recommendation systems, and autonomous vehicles. It’s particularly well-suited for situations where explicit training data is limited, and the agent must learn from its own experiences to achieve optimal results.