Definition: A machine learning approach where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties.
Better definition: When your computer learns by trial and error, like a mouse in a maze searching for cheese.
Where does this fit in the AI Landscape?
Reinforcement learning is an important AI technique that has gained prominence in recent years, enabling breakthroughs in robotics, gaming, and autonomous systems. It's been successfully applied to complex decision-making tasks, such as optimizing supply chains and developing self-driving cars, and continues to inspire new applications.
What are the real world impacts of this?
Reinforcement Learning is behind many technologies, from recommendation systems to game-playing AI like AlphaGo. It can create systems that adapt and improve over time, offering more personalized and efficient services. For developers, reinforcement learning presents opportunities to work on dynamic and self-improving AI systems.
What could go wrong in the real world with this?
A reinforcement learning agent is taught to optimize the perfect movie night, but instead of balancing various factors, it insists on marathons of obscure 1970s kung fu movies.
Can be used to continually improve the model's performance by letting it learn from its mistakes and successes in code autocompletion and chat responses.