I built an open-source roguelike specifically for training game-playing agents [P]

Hey everyone! I wanted to share something I’ve been working on. I was inspired by projects from DeepMind and OpenAI, but noticed that most games are prohibitively difficult to integrate with an agent harness. So I built DelveRL from the ground up as a human-playable game with a structured API, deterministic simulation, procedural levels, partial observability, and enough strategic headroom for agents to compete and improve. It’s an endless turn-based roguelike where agents must explore, manage risk and resources, fight enemies, and escape each floor. Everything runs locally, including batched renderer-free environments and a recurrent PPO trainer. The included baseline reaches a median floor of 18, with extended runs reaching floor 33. The game, training code, checkpoint, bridge documentation, and raw benchmarks are all open source. I’d love to see what approaches people try - and how quickly the baseline gets crushed submitted by /u/SnyderConsulting [link] [留言]