Reinforcement Learning
Reinforcement learning is a method that lets AI learn through trial and error, much like training a pet: rewards for doing things right, penalties for doing them wrong, until it learns to complete a specific task.
How it works
In reinforcement learning, a program called an “agent” interacts with an environment and adjusts its strategy based on the outcomes of its actions (rewards or penalties). At its core, the approach lets the AI explore on its own and discover the best path to its goal, without humans providing step-by-step instructions — making it well suited to solving complex sequential decision-making problems.
Examples
- 1.Training an AI to play video games at a high level
- 2.Optimizing the movements of industrial robots to complete assembly efficiently
- 3.Developing self-driving systems that plan routes dynamically based on traffic conditions and passenger demand