Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

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Justin Boyan, Michael Littman


This paper describes the Q-routing algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used by each node to keep accurate statistics on which routing decisions lead to minimal delivery times. In simple experiments involving a 36-node, irregularly connected network, Q-routing proves supe(cid:173) rior to a nonadaptive algorithm based on precomputed shortest paths and is able to route efficiently even when critical aspects of the simulation, such as the network load, are allowed to vary dy(cid:173) namically. The paper concludes with a discussion of the tradeoff between discovering shortcuts and maintaining stable policies.