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I’ve implemented an agent using deep reinforcement learning (with Q-Learning) that plays Tetris (not sure if it plays forever, but it seems to). It makes a decision based on the state that is expected to provide a higher reward in the future (i.e. it’s not greedy, so it will, for example, wait to clear multiple lines instead of a single one).
Here’s the source code (made in Python with Keras + Tensorflow)
I appreciate any feedback.
submitted by /u/artificial-thinking
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