Complexity Simplified #5: Reinforcement Learning?

Reinforcement learning is basically to it's core teaching through experience, similar to how humans learn from mistakes.

So What Is It?

Reinforcement learning is when an AI learns through trial and error runs, It doesn't need a guidebook of rules, it explores its world and figures out what works based on feedback.

How it Works: The "Game" of RL

To understand RL, imagine a video game, in this case RL or Rocket League, with four main parts. Think about this, you are playing rocket league (a soccer game) and you get scored on again and again, so you try new patterns until something starts working. That's how you learn through trial and error. AI is basically the same thing:

• The Agent: The "player" (or the AI system).

• The Environment: The "soccer field", or world the agent is in.

• The Action: The move the agent makes (like jumping or flipping).

• The Reward: The "score." Doing something good gets a "treat" (positive reward); doing something bad gets a "penalty" (negative reward). In the case of the game, it's getting score on or scoring a point.

  
  

The Learning Loop

1. Observe: The agent looks at where it is.

2. Act: It tries a move.

3. Feedback: The environment gives it a reward or penalty.

4. Update: The agent remembers this result so it can make a better choice next time, and learns progressively.

Why is it useful?

Because RL agents can learn on their own, they excel in unpredictable situations where humans can't write every rule: 

• Robotics: Helping robots walk or pick up objects without bumping into things.

• Games: Mastering complex strategy games like Chess or Go.

• Sustainability: Managing power in data centers to save energy. 

  
  

Summarize

This is how over the years AI went from being slop and ignored by the world, to more recently used and relied on by so many people to a point where they can't imagine their life without AI. If you are one of those people, you have Reinforcement Learning to thank.

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