3 papers
cs.LG2026
NashDreamer: Model-Based Reinforcement Learning for Zero-Sum Imperfect-Information Games
Tomáš Holeček, Viliam Lisý
Model-based reinforcement learning (MBRL) has achieved remarkable results in single-agent domains, yet its extension to competitive imperfect information games (IIGs) remains under…
cs.GT2026
Test-time Reinforcement Learning in Imperfect Information Games
Ondrej Kubicek, Viliam Lisy, Tuomas Sandholm
Test-time reasoning has significantly improved performance in domains ranging from games to language models. However, test-time policy changes with formal guarantees on the perform…
cs.LG2026
Superhuman AI for Generals.io Using Self-Play Reinforcement Learning
Matej Straka, Viliam Lisý, Martin Schmid
We present a superhuman AI agent for Generals.io, a real-time strategy game that requires both long-horizon planning and short-term tactics under strong imperfect information. Trai…