How does reinforcement learning work?

Can you explain how reinforcement learning works?

Sure! Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards.

What are the key components of reinforcement learning?

The key components include the agent, environment, actions, rewards, and the policy that the agent follows to decide its actions.

How does the agent learn from its actions?

The agent learns through trial and error, receiving feedback in the form of rewards or penalties, which helps it improve its policy over time.

Can you give an example of reinforcement learning in action?

A classic example is training a robot to navigate a maze. The robot receives rewards for reaching the goal and penalties for hitting walls, guiding its learning process.