Reinforcement Learning
Reinforcement Learning is a type of AI where a software agent learns to make decisions by taking actions in an environment and receiving rewards or penalties for its choices, much like training a pet with treats.
How it works
It works through a trial-and-error process where the agent aims to maximize its cumulative reward over time. This approach is fundamental for teaching AI to master complex, sequential tasks where explicit instructions are difficult to provide.
Examples
- 1.A computer program learning to play a video game by practicing millions of times.
- 2.An autonomous vehicle learning to navigate safely by simulating countless driving scenarios.
- 3.A robotic arm learning the precise movements to assemble a product.