I'm building an AI agent that plays Slay the Spire. It currently reaches A20 Act 3 consistently and occasionally defeats the A20 Heart. It use two search: spare graph search for deck building, and MCTS for combat.
Parallelly, I'm working on an attention-based memory retrieval system that achieved SOTA on LongMemEval, LoCoMo, and code retrieval benchmarks. https://github.com/AttemorySystem/attemory/
Maybe later I'll build a live-streaming AI agent that plays Slay the Spire while conversing with viewers and remembering everything in chat!
Simply feeding the Slay the Spire game state directly to an LLM is currently insufficient to achieve stable deck-building or consistent combat performance.
I categorize the game's actions into three types: combat, deck-building, and other interactions.
For combat, MCTS is used. Yes, it's similar to the approach used in Go AI.
For deck-building, a sparse graph search is used: the goal is to rapidly identify winning deck templates within the graph structure.
Only the remaining aspects are delegated to the LLM to make reasoned decisions.
Parallelly, I'm working on an attention-based memory retrieval system that achieved SOTA on LongMemEval, LoCoMo, and code retrieval benchmarks. https://github.com/AttemorySystem/attemory/
Maybe later I'll build a live-streaming AI agent that plays Slay the Spire while conversing with viewers and remembering everything in chat!