Reinforcement Learning

Lesson goals

• Agent and environment
• State, action, and reward
• Exploration versus exploitation

Core idea

Learning decisions through interaction and rewards.

1. Agent and environment: understand it in context, identify inputs and outputs, and define how the result will be verified.

2. State, action, and reward: understand it in context, identify inputs and outputs, and define how the result will be verified.

3. Exploration versus exploitation: understand it in context, identify inputs and outputs, and define how the result will be verified.

Practical example

Consider a real application of “Reinforcement Learning.” Start small, identify available data, and compare the system with a clear baseline before expanding it.

Check your understanding

1. Explain “Reinforcement Learning” in your own words.
2. Give one appropriate and one misleading application.
3. What risk or limitation should be monitored?