HySTAR: Anchored Hypergraphs for Stable Credit Assignment in Cooperative Multi-Agent Reinforcement Learning

arXiv:2609.31531 · cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Hypergraph — 24× in this paper · explained in 2 episodes
• Actor — 21× in this paper · explained in 3 episodes
• Reinforcement Learning — 20× in this paper · explained in 4 episodes
• Credit assignment — 10× in this paper · explained in 3 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Cooperative Multi-Agent Reinforcement Learning — 3× in this paper
• Representation Learning — 3× in this paper · explained in 2 episodes
• Reward Shaping — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Anchored Hypergraph Value Decomposition — 2× in this paper
• Benchmark — 2× in this paper · explained in 2 episodes
• Google Research Football — 2× in this paper
• Parameter Efficiency — 2× in this paper · explained in 3 episodes
• Q-learning — 2× in this paper · explained in 3 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Dec-POMDP — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Multi-Agent Reinforcement Learning (MARL) — 1× in this paper · explained in 4 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Spectral clustering — 1× in this paper · explained in 2 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
• Rethinking the Implementation Tricks and Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning
• TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations
• Deep Multi-Agent Reinforcement Learning with Relevance Graphs

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