Wanted to try out reinforcement learning for a few years. Decided to apply this to as a warm up problem

What it does

A neural network attempts to optimize it's high score at 2048

How I built it

Forked a university assignment codebase that had 2048 in python. Essentially rewrote the entire thing. Wrapped this in an OpenAI gym environment. Used a blog post that applies reinforcement learning to a different OpenAI gym.

Challenges I ran into

Original codebase was more confusing than helpful. Reinforcement learning seems to converge much slower than supervised learning.

Accomplishments that I'm proud of

Getting a clean codebase up. Catching bugs with unit tests. Will be proud if I can get good performance from the neural net.

What I learned

Training difficulty is nontrivial, even for such a simple game / state place.

What's next for RL-asmus

Try using RL to tune bicycle wheels (in real world)

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