HTC Seminar Series #11: Introduction to Deep Reinforcement Learning
This lecture is a good introduction to what deep reinforcement learning is, and why you might want to try and understand how it works. You can think of deep reinforcement learning as the shotgun marriage of deep learning and reinforcement learning.
Reinforcement learning has been around in some form since the very beginning of machine learning and artificial intelligence research. It also has a long history of being used in the video game industry. Since it's a war horse of old AI approaches that didn't really pan out in the end, why do we care about it in 2020?
Deep learning is the secret sauce that allows reinforcement learning algorithms to jump into the modern AI playpen and duke it out with all of the other algorithms competing in there for the title of 'smartest machine learner'. Literally battling it out if you are talking about video gameplay algorithms.
Extending reinforcement learning by incorporating deep learning into it allows the algorithm to learn from model data. So you can use reinforcement learning without requiring an explicitly designed state space. By design we mean someone hand crafted it. Getting rid of this 'hand design' part by incorporating deep learning into the algorithm allows reinforcement learning to solve hard problems it could not solve before (sound familiar).
DeepMind has used deep reinforcement learning to achieve human levels of performance in a wide variety of different video games.
If you are dying to learn more about deep reinforcement learning, and how to implement it in TensorFlow, check out this talk from Google I/O 2018.
Lex also has a really interesting series of artificial intelligence podcasts that we might dip into for some future HTC seminars.
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