Multi-agent deep learning for simultaneous optimization for time and energy in distributed routing system

Packet routing problem most commonly emerges in the context of computer networks, thus the majority of routing algorithms existing nowadays is designed specifically for routing in computer networks. However, in the logistics domain, many problems can be formulated in terms of packet routing, e.g. in automated traffic routing or material handling systems. In this paper, we propose an algorithm for packet routing in such heterogeneous environments. Our approach is based on deep reinforcement learning networks combined with link-state protocol and preliminary supervised learning. Similarly to most routing algorithms, the proposed algorithm is a distributed one and is designed to run on a network constructed from interconnected routers. Unlike most other algorithms, proposed one views routers as learning agents, representing the routing problem as a multi-agent reinforcement learning problem. Modeling each router as a deep neural network allows each router to account for heterogeneous data about its environment, allowing for optimization of more complex cost functions, like in case of simultaneous optimization of bag delivery time and energy consumption in a baggage handling system. We tested the algorithm using manually constructed simulation models of computer network and baggage handling system. It outperforms state-of-the-art routing algorithms.