Playing 2048

Playing 2048 using Proximal Policy Optimization. Lots of TODO's before it really performs well. However, the agent already learns to beat the game after approx. 8 hours of training:

Testing

I use pytest. Run the following commands:

• pytest
• pytest --doctest-modules

TODOS

• Use model wrappers to convert observation space instead of what is done now.
• Add setup.py for installation of dependencies.
• One-hot encoding of observation space instead of numerical values. Probably will be a better representation?