reinforcement-learning

Vocabulary Word

Definition

Reinforcement-learning is a type of machine learning technique that enables a software agent to learn by interacting with its environment. It’s like placing a robot in a room, and it learns to navigate through repeated trial and error and rewards or penalties.

Examples in Different Contexts

In machine learning, reinforcement learning is used to solve complex problems where the algorithm learns by interacting with its environment. A machine learning engineer might explain, 'We use reinforcement learning for applications like game playing and autonomous driving, where the system learns optimal strategies through trial and error.'

Practice Scenarios

Tech

Scenario:

Our self-driving cars need to rapidly adapt to situations on the road. It’s important they can respond effectively to unknown scenarios.

Response:

We could make use of reinforcement learning in our self-driving cars. It'll help them adapt and respond to unpredictable road situations better.

Robotics

Scenario:

To improve our waste sorting process, we need a more intelligent mechanism.

Response:

By implementing reinforcement learning, our robots can learn to sort waste more efficiently and with greater accuracy.