Tiny intro to RL 🤖

“Reinforcement Learning: It’s like playing a board game with the world, where every move teaches you something new.” — Inspired by Jeff Dean

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Why RL matters?

Imagine you’re playing a game of chess♟️. You’re a beginner, and you don’t know the rules. You make a move, and your opponent tells you it’s illegal. You try another move, and this time it’s allowed. As the game progresses, you start to understand the rules and strategies better. You learn from your mistakes and improve your game. This is exactly how reinforcement learning works and it allows machines to learn from their experiences and improves over time. It has a wide range of applications like optimizing business process like Kiva Systems (Amazon renamed it as Amazon Robotics) provides E-commerce Warehouse Optimization through their automated guided vehicles. The possibilities are endless.

Little history of RL

The story of Reinforcement Learning (RL) is a fascinating journey that spans over a century. It begins in the early 20th century with Edward Thorndike, who describe the essence of trial-and-error learning with the “Law of Effect” in 1911. In 1927, Pavlov formally used the term “reinforcement” in context of animal learning. In 1980’s the development of Q-Learning (an algorithm in RL) was a significant milestone in the field and followed by introduction of Deep Reinforcement Learning. Then in 21st century the following are the recent achievements in RL…

Achievements in RL

• In gaming, a model named BBF (Bigger,Better,Faster) learns 26 games of Atari and performs at superhuman level.
• ChatGPT is trained on unique training model called Reinforcement Learning from Human Feedback (RLHF)
• Reinforcement learning is used to land the rocket 🚀 on the surface in a simulated environment, but RL has a potential to use it in real world rockets.
• MIT has introduced the Liquid Neural Network, a method that can enhance Deep Reinforcement Learning. Remarkably, this network utilizes only 19 artificial neurons to automate a drone.

Embark on a brief yet insightful journey 🚀into the world of Reinforcement Learning with me. This is just the beginning, with more in-depth articles soon to be published on Medium. Stay tuned for more! Feel free to connect with us on social media (X, LinkedIn) by sending a direct message. I am looking forward to engaging with you 😊