A Semi-Decentralized Approach to Scalable Multiagent Control

Scalable multiagent control under stochastic communication remains an open challenge. Existing planning methods such as RS-SDA${}^{\star }$ scale poorly, sampling-based approaches such as Dec-MCTS assume fixed-period communication, and MARL methods typically impose either strict execution-time decentralization (QMIX, MAPPO) or full centralization (MAZero), respectively limiting coordination opportunities or ignoring the stochastic communication constraints that arise at decision time. We consider the Semi-Decentralized POMDP (SDecPOMDP), which unifies the Dec-POMDP and MPOMDP through a distribution over communication-sojourn times. We introduce SDecMCTS, a multiagent MCTS algorithm that achieves solution quality comparable to RS-SDA${}^{\star }$ while requiring less computation across benchmarks. Building on the SDecPOMDP's semi-Markov communication dynamics, SDecMCTS uses an options framework in which decentralized solvers execute between communication events, enabling planning and execution that faithfully reflects semi-decentralization. We extend SDecMCTS to Semi-Decentralized Zero (SDecZero), a scalable AlphaZero-style algorithm that learns semi-decentralized value estimates used by decentralized solvers. SDecZero additionally learns the stochastic communication dynamics through a dedicated network communication head. SDecZero substantially outperforms model-based and model-free methods across large multi-drone search scenarios constructed from real cave systems associated with the DARPA Subterranean Challenge.