Building a Contextual Policy Ranker

PlayChess explores how a compact model can rank actions using the context surrounding a position rather than treating every decision independently.

The system uses a listwise objective to learn relative preferences across candidate actions. This makes the ranking signal useful even when several moves are reasonable.

Keeping the model around 0.9 million parameters makes experimentation fast and keeps the project practical to train and deploy.