Zachary Ruhe
Molecular biologist (Ph.D., UCSB) who builds machine learning systems. I spent thirteen years at the bench on protein–protein interactions and bacterial toxin delivery, published ten first-author papers including one in Cell, then taught myself to program in 2020 and built the machine learning inside an FDA-cleared clinical diagnostic.
The repositories here are reinforcement learning work done on my own time, on a single consumer GPU. I design the architectures, write the training loops, and build the environments the agents learn in.
orbitwars — AlphaFold's Evoformer, applied outside biology
A Kaggle strategy environment turned out to have the same shape as a folded protein: entities, and the relationships between them. I adapted the Evoformer's node/pair message-passing — an explicit learned edge state, pair-biased attention, and tokens writing back into the edges each layer. Trained by imitation, then self-play PPO with a joint action distribution over the whole board.
ps-ppo — transformer reinforcement learning for Pokémon Showdown
Behavioral cloning, then distributed PPO across 250M+ states in two days on one RTX 3090. A custom attention mask isolates the state representation from the actor and critic heads, forcing the backbone to build an unbiased view of the game. 1900+ ELO from a pure neural policy — no tree search, expectimax, or damage calculator at inference. The published MLP baseline, reproduced under its own hyperparameters, plateaus near 1100.
SimpleTFTEnv — an environment built to isolate a problem
Simultaneous action selection, hidden information, and irreducible stochasticity, at a fraction of the compute. Built because the interesting failures in the Pokémon work needed a cheaper place to study them.
BFF — computational abiogenesis
Self-replicating instruction sequences emerging from random initialization in a 3D grid, under spatial constraint and simple interaction rules.
Interested in reinforcement learning, emergent behavior, and evaluation — specifically, how you tell whether a model is right for the right reasons.