I tried to combine Go, Python, and Rust and this is where I ended up

So reinforcement learning is basically just trial-and-error with a fancy name and a PhD thesis attached to it. You know, that thing where your ML model randomly tries stuff until something works, collects its reward, and pretends it knew what it was doing all along. It's like training a dog, except the dog is a neural network, the treats are loss functions, and you have no idea why it suddenly learned to recognize cats after 10,000 epochs of complete chaos. The best part? Data scientists will spend months tuning hyperparameters when they could've just... thrown spaghetti at the wall and documented whatever didn't fall off. Q-learning? More like "Q: Why is this working? A: Nobody knows."

Content FREEZE CS 4620 Intelligent Systems Changing random stuff until your program works is "hacky" and "bad coding practice." But if you do it fast enough it is "Machine Learning" and pays 4x your current salary.

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