Explanation
Imagine training a puppy to fetch a ball. You don't tell it exactly how to run, grab, and return. Instead, you reward it with a treat when it gets closer to the desired behaviour.
Reinforcement learning is similar. It's a type of machine learning where an "agent" learns to make decisions in an environment to maximise a reward.
The agent interacts with the environment, takes actions, and receives feedback in the form of rewards or penalties.
Over time, the agent learns the optimal strategy or policy to achieve the highest cumulative reward.
It's learning through trial and error, constantly refining its approach based on the consequences of its actions.
Think of it as a digital experiment where the computer learns by doing and adapting.
Examples
Consumer Example
Consider a video game AI that learns to play a game like chess or Go.
The AI explores different moves, learns from its mistakes, and gradually improves its strategy to win more often.
It's like having a virtual opponent that constantly challenges itself to become a better player.
Business Example
Imagine a company optimising its pricing strategy for a product.
Reinforcement learning can analyse market data, customer behaviour, and competitor pricing to dynamically adjust prices in real-time.
The system learns which prices lead to the highest revenue and adjusts accordingly.
It's like having an automated pricing expert that constantly seeks the optimal balance between sales volume and profit margin.