Benefits of Deep Reinforcement Learning for Resource Allocation in Optical Networks

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In this project several resource allocation problems in dynamic elastic optical networks will be studied with the aim of identifying the scenarios and configurations where DRL agents can offer a significant advantage over existing solutions, whilst keeping an acceptable computational complexity. The main research objective is to understand the characteristics that give DRL an advantage in the problem of resource allocation in optical networks: 1. Identify the characteristics that give a DRL approach an advantage in solving resource allocation problems in optical networks with respect to the best heuristics known to date; and 2. Identify the fundamental parameters of the resource allocation problems that allow for improved performance of DRL agents with respect to current solutions.