SkyNet: Belief-Aware Planning for Partially-Observable Stochastic Games

📰 ArXiv cs.AI

SkyNet introduces belief-aware planning for partially-observable stochastic games, extending MuZero to multi-player environments with uncertainty

advanced Published 31 Mar 2026

Action Steps

Extend MuZero to partially-observable stochastic games
Combine learned dynamics models with Monte Carlo Tree Search (MCTS)
Incorporate belief-aware planning to handle uncertainty about hidden state
Apply to multi-player environments, such as card games or other domains with incomplete information

Who Needs to Know This

AI researchers and engineers working on multi-agent systems and reinforcement learning can benefit from SkyNet, as it enables more effective decision-making in complex, uncertain environments

Key Insight

💡 SkyNet enables effective decision-making in complex, uncertain environments by incorporating belief-aware planning into model-based reinforcement learning