Improving Entity-Configuration Generalization in Real-Time Strategy Games with Task Hypergraph Representation

This paper investigates compositional generalization across entity configurations in real-time strategy (RTS) games, which remains challenging for existing methods. To address this problem, we propose a task-hypergraph-based policy architecture that represents game entities as nodes and task instances involving multiple entities, such as resource-gathering and attacking, as hyperedges. The architecture comprises a task hypergraph constructor and a task hypergraph network. We encode RTS domain knowledge as cross-scenario task schemas, feasibility constraints, and an objective function over task-instance sets, thereby formulating hypergraph construction as a constrained optimization problem over sets of task instances. Given the current state, the constructor instantiates schema-valid task instances and selects a jointly feasible subset using the set-level objective. The selected instances then induce the task hypergraph. The task hypergraph network aggregates information among entities participating in the task instance through node-to-hyperedge-to-node message passing and generates entity-level actions using multiple action heads, relation encodings, and hyperedge-type-dependent soft biases. By representing changes in entity configurations as recombinations of task instances and their participating entities under shared task schemas, our approach enables consistent task-level processing across scenarios and thereby supports compositional generalization.