Risk-Calibrated Communication and Adaptive Safe Recovery in Hierarchical Multi-Agent Reinforcement Learning Under Compound Communication Failures

Cooperative multi-agent reinforcement learning (MARL) relies on communication to coordinate under partial observability. Real channels lose, delay, and corrupt messages, and a compromised agent can send misleading ones. In hierarchical MARL, these failures propagate into task-allocation errors and unsafe low-level actions. Prior work has studied message filtering, communication-constrained learning, and hierarchical recovery under message loss largely in isolation. This paper proposes RiCoR, a risk-calibrated communication and recovery framework for hierarchical MARL. RiCoR separates observable channel conditions from message credibility and represents uncertainty in its reliability estimates with an evidential Beta posterior. It gates each message by a lower confidence bound whose threshold rises with the downstream decision risk, measured by a conditional value-at-risk (CVaR) cost critic. A learned mode selector switches among normal coordination, selective reliance, active verification, and local fallback, with hysteresis and explicit switching and verification costs. A one-step safety shield filters low-level actions, and its guarantee is stated under explicit assumptions. The paper also specifies an evaluation protocol for a hierarchical wildfire-suppression benchmark with one coordinator and eight regional agents. The protocol covers isolated, compound, and held-out faults, four baselines, and calibration, recovery, ablation, scalability, and overhead analyses. The paper reports no empirical outcomes.