Chimera: Pioneering Generalized and Adaptable Intelligent Routing in MANETs

Recent routing algorithms show the potential of deep reinforcement learning (DRL) for optimal routing in mobile ad-hoc networks (MANETs). However, existing DRL-based methods still have limitations in generalization and adaptability when facing unseen scenarios and mutations under local observation constraints. In this paper, we propose Chimera, a DRL-based routing algorithm for MANETs, which achieves adaptive routing and generalizes to changing environments. Chimera contains a unified-policy multi-agent framework combining traditional routing principles and Policy Pool. Specifically, we introduce Policy Pool, a mechanism where routers select the most suitable policy from multiple policies, enabling adaptation to diverse situations without extensive parameters or real-time adjustments. To achieve overall optimization, we introduce a unified-policy multi-agent framework, where a unified routing strategy across sharing-parameters devices ensures flexibility in diverse networks. Instead of purely DRL-based routing algorithms, we combine traditional methods and DRL algorithms for the robustness of Chimera by introducing the traditional protocol as a policy in Policy Pool and leveraging routing exploration for local observation supplement. Our experimental evaluations demonstrate that Chimera outperforms rule-based and DRL-based routing algorithms in learning effective routing policies, where Chimera achieves an average of 19% higher throughput, 14% less packet loss, and 22% lower delay compared to the best baseline in various MANET scenarios.