Reinforcement Learning for the Game of Balatro: A Fast Simulator and Two-Policy Training Approach
arXiv:2610.39049v1 Announce Type: cross Abstract: Balatro, a 2024 roguelike deckbuilder, presents three difficulties that have historically derailed reinforcement learning methods: a 3,000-action state-dependent action space, horizons of several hundred decisions with sparse rewards, and pervasive stochasticity in both card draws and shop generation. Published RL results on the game remain scarce. We introduce an end-to-end pipeline comprising (i) a high-throughput Rust simulator that is automa…