What is Reinforcement Learning?

Reinforcement learning is a machine learning approach that trains models through trial and error, using feedback to optimize actions towards a specific goal. It is one of the three main types of machine learning, alongside supervised and unsupervised learning, and is characterized by its use of reward signals to reinforce successful actions and penalize unsuccessful ones. This method is particularly suited for tasks that involve strategic decision-making, such as self-driving cars, gaming AI, and optimizing supply chains, as it enables models to learn and improve autonomously by cycling through numerous iterations. Unlike supervised learning, which relies on labeled data, reinforcement learning depends on the model's interaction with its environment to learn from actions and feedback. The technique is especially powerful in closed systems with clear rules, where models can be trained in simulated environments, allowing for rapid iteration and improvement. Companies like Seldon, which specialize in deploying machine learning models, leverage reinforcement learning to transform complex tasks into strategic advantages, offering flexibility and efficiency in various applications.