Author

Viliam Lisý

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Preprint Sep 2026

Regularized policy gradient with learned mixtures of Gaussians for games with continuous actions

Most successes of superhuman game-playing algorithms are in games with discrete actions, yet in auctions, robotics, sports, or trading, actions are nearly continuous. Prior techniques either rely on expert-designed discretizations or are sample inefficient. We present a scalable policy-gradient algorithm for large sequ...

Ondrej Kubícek, Viliam Lisý, Tuomas Sandholm · 0 citations

#small language model Preprint Aug 2026

Test-time Reinforcement Learning in Imperfect Information Games

This work extends the concept of gadget game, tabular technique for test-time search, to the reinforcement learning setting and formally proves that, unlike prior tabular algorithms, regularized policy-gradient algorithms limit possible strategy degradation caused by test-time reasoning, even without the gadget games.

Ondrej Kubícek, Viliam Lisý, Tuomas Sandholm · 1 citation

#machine learning Preprint Sep 2026

NashDreamer: Model-Based Reinforcement Learning for Zero-Sum Imperfect-Information Games

NashDreamer is proposed, a principled MBRL framework for two-player zero-sum IIGs that introduces a centralized Multi-Agent Recurrent State-Space Model (MARSSM) that decouples environment dynamics from the effect of players's strategies on their individual observations.

Tomáš Holeček, Viliam Lisý · 0 citations