AI RESEARCH
SkyNet: Belief-Aware Planning for Partially-Observable Stochastic Games
arXiv CS.AI
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ArXi:2603.27751v1 Announce Type: new In 2019, Google DeepMind released MuZero, a model-based reinforcement learning method that achieves strong results in perfect-information games by combining learned dynamics models with Monte Carlo Tree Search (MCTS). However, comparatively little work has extended MuZero to partially observable, stochastic, multi-player environments, where agents must act under uncertainty about hidden state. Such settings arise not only in card games but in domains such as autonomous negotiation, financial trading, and multi-agent robotics.