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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.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 combines long-horizon planning, imperfect information, and combinatorially large state and action s…
cs.LG2020★ 1 cited
Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks and Autoregressive Policy Decomposition
Jaromír Janisch, Tomáš Pevný, Viliam Lisý
We focus on reinforcement learning (RL) in relational problems that are naturally defined in terms of objects, their relations, and object-centric actions. These problems are chara…