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For an ai-class project I need to implement a reinforcement learning algorithm which beats a simple game of tetris. The game is written in Java and we have the source code. I know the basics of reinforcement learning theory but was wondering if anyone in the SO community had hands on experience with this type of thing.
Edit: The more specific the better, but general resources about the subject are welcomed.
Follow up:
Thought it would be nice if I posted a followup.
Here's the solution (code and writeup) I ended up with for any future students :).
Paper / Code
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Deep Reinforcement Learning in Python (Udemy) Reinforcement Learning is just another part of artificial intelligence; there is much more than that like deep learning, neural networks, etc. This course from Udemy will teach you all about the application of deep learning, neural networks to reinforcement learning.
Three methods for reinforcement learning are 1) Value-based 2) Policy-based and Model based learning. Agent, State, Reward, Environment, Value function Model of the environment, Model based methods, are some important terms using in RL learning method.
Take a look at the 2009 RL-competition. One of the problem domains is a tetris game. There was a tetris problem the year before too. Here’s the 52-page final report from that year’s fifth-place finalist, which goes into a lot of detail about how the agent worked.
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The Heaton Research ebook is quite good at explaining neural network concepts (with code). Chapter 4 is dedicated to machine learning and the various training methods for your networks. There is a downloadable library and sample applications for you to look at.
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