📒 AI Terms Daily Dose: Reinforcement Learning (RL)

Michael Wacht

🔥

Aug 26 (edited) • General Discussion 💬

Term: Reinforcement Learning (RL)

Day: 16

Level: Advanced

Category: Learning & Models

🪄 Simple Definition:

A way AI learns by trial and error — getting “rewards” for good actions and “penalties” for bad ones.

🌟 Expanded Definition:

Reinforcement Learning is a machine learning approach where an agent interacts with an environment, takes actions, and learns from feedback in the form of rewards or punishments. Over time, the agent optimizes its behavior to maximize rewards. RL is especially useful for problems involving sequences of decisions, uncertainty, or long-term outcomes.

⚡ In Action:

Robots trained with reinforcement learning learn how to walk, balance, or pick up objects by experimenting thousands of times and being rewarded for correct movements.

💡 AIS+ Pro Tip:

RL is powerful but data-hungry and slow to train. Combine it with simulations or use hybrid methods (like imitation learning) to speed up training before deploying in the real world.

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https://www.skool.com/ai-automation-society/ai-terms-everyone-should-know-series?p=91d0578a

Series inspired by the "Only Cheatsheet to Master AI Basics" post by @Yash Chauhan found at:

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