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States, Actions, Rewards — The Intuition behind Reinforcement Learning


In 2014, Google acquired a British startup named DeepMind for half a billion dollars. A steep price, but the investment seems to have paid off many times over just from the publicity that DeepMind generates. ML researchers know DeepMind for its frequent breakthroughs in the field of deep reinforcement learning. But the company has also captured the attention of the general public, particularly due its successes in building an algorithm to play the game of Go. Considering the frequency with which DeepMind makes progress in the field — AlphaGo, AlphaGo Zero and AlphaZero in the last couple of years — it becomes hard to keep track of what exactly is happening, both at a technical level and from the point of view of implications. I plan to do just that — provide a high-level view of DeepMind’s successes in Go, and explain the distinctions between the different versions of AlphaGo that they have produced.

But first, for context, I’d like to give a overview of reinforcement learning (RL) in general, and that is what the rest of this particular post is dedicated to.

Continue reading on Medium » Deepak Dilipkumar February 13th 2020

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