Reinforcement learning / reverse engineering / CUDA
1942//PPO
I trained a reinforcement-learning agent to complete the NES game 1942 from a fresh power-on.
No action tape, scripted controller, manual assistance, or last-second rescue. The final policy cleared all 32 stages in one uninterrupted run while making 64,178 controller decisions on its own.
The project combines NES emulation, raw RAM observation, PyTorch PPO training, CUDA acceleration, automated evaluation, and enough debugging to make me briefly hate the year 1942.