Welcome to Cutting–Edge AI!
This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course.
Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks).
While both of these have been around for quite some time, it’s only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning.
The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer.
Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be.
We’ve seen how AlphaZero can master the game of Go using only self–play.
This is just a few years after the original AlphaGo already beat a world champion in Go.
We’ve seen real–world robots learn how to walk, and even recover after being kicked over, despite only being trained using simulation.
Simulation is nice because it doesn’t require actual hardware, which is expensive. If your agent falls down, no real damage is done.
We’ve seen real–world robots learn hand dexterity, which is no small feat.
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Specification: Cutting-Edge AI: Deep Reinforcement Learning in Python
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