Teaching a machine to play Slay the Spire like a top streamer.
Slay the Spire is a deckbuilding roguelike with enormous branching: hundreds of cards and relics, random draws, hidden enemy intents, and a 50+ floor run where one bad fight ends everything. Spire Pilot is a from-scratch AI that plays Ascension 20 Ironclad — the hardest difficulty — aiming to win as reliably as a skilled human. Below is the architecture at a glance, and the live runs page shows every decision it makes, as it makes them.
This is a research system in active training, not a finished bot. It has no verified A20 win yet. The ~90% figure is the goal — roughly a skilled Twitch streamer — and every training cycle chips away at the gap. Current, real numbers live on the live runs page — watch it play, wins and deaths alike.
Get your runs reviewed
Upload a run and the A20 bot reviews every decision: which card it would have drafted and why, how much HP each fight should have cost, and — for any fight — a step-through replay of the bot playing your exact spot. Join the waitlist and be first in when it opens.
Two brains, one run
A run splits cleanly into two decision problems, and the AI uses a dedicated model for each. They talk to each other: the map brain asks the combat brain "how much will this fight hurt?"
The Combat Brain
Inside a fight, every turn: which cards to play, in what order, on which target. It sees the literal battle state as a set of typed tokens and runs a Monte-Carlo tree search guided by a transformer that predicts the fight's outcome.
- Input tokenized battle state
- Model entity-token transformer
- Decision MCTS + CVaR risk selection
The Out-of-Combat Brain
Between fights: which path to take on the map, which cards to add or remove, what to buy, how to resolve events. It scores whole run states with a win-probability net that reasons over your owned set of cards and relics.
- Input deck + relics + run context
- Model owned-set attention net
- Decision path planner over win-prob
One specialist network per encounter
There is no single all-purpose combat AI. Every boss, elite, and hallway fight has its own neural network - a Slime Boss specialist, a Gremlin Nob specialist, and so on - because each fight punishes different mistakes. When a fight starts, the matching specialist is loaded.
The specialist reads the whole battle as a set of tokens, one per thing on the board:
The network has several output "heads" - separate predictions made from the same read of the battle:
The auxiliary heads never pick a move. They exist to force the network to actually understand the fight: a net that has to predict next turn's incoming damage must model enemy intents, not just guess.
On top of the network runs a Monte Carlo tree search written in C++. Live, it simulates about 1200 possible futures per decision:
Every choice priced in win probability
A separate run-level model estimates the probability of finishing the run - and beating the Heart - from any map state: the deck with upgrades, relics, potions, current HP, gold, and the actual map layout ahead.
Every out-of-combat decision is scored the same way: "how does win probability change if I take this?"
Card rewards and removals
A card joins the deck only if the run wins more often with it than without it. Skipping is a real option, and so is paying to remove a Strike.
Shops and campfires
Buy, or save the gold. Rest, or smith an upgrade. Each option is a different future run, and each future gets a win probability.
Pathing
The planner reads the actual map: which rooms a route visits, which fights it forces, and what those fights will cost.
The flywheel: how the models get better
The models improve in a loop: play, mine the failures, retrain, and only ship a new version when it is provably no worse anywhere and better somewhere.