How does reinforcement learning work?

Can you explain how reinforcement learning works?

Sure! Reinforcement learning (RL) 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, states, and rewards. The agent interacts with the environment and learns from the feedback it receives.

How does the agent learn from rewards?

The agent uses a trial-and-error approach, adjusting its actions based on the rewards received. It aims to learn a policy that maximizes future rewards.

Can you give an example of reinforcement learning in action?

Certainly! A classic example is training a robot to navigate a maze. The robot receives positive rewards for reaching the goal and negative rewards for hitting walls, helping it learn the best path.