  Communication load balancing aims to balance the load between different
available resources, and thus improve the quality of service for network
systems. After formulating the load balancing (LB) as a Markov decision process
problem, reinforcement learning (RL) has recently proven effective in
addressing the LB problem. To leverage the benefits of classical RL for load
balancing, however, we need an explicit reward definition. Engineering this
reward function is challenging, because it involves the need for expert
knowledge and there lacks a general consensus on the form of an optimal reward
function. In this work, we tackle the communication load balancing problem from
an inverse reinforcement learning (IRL) approach. To the best of our knowledge,
this is the first time IRL has been successfully applied in the field of
communication load balancing. Specifically, first, we infer a reward function
from a set of demonstrations, and then learn a reinforcement learning load
balancing policy with the inferred reward function. Compared to classical
RL-based solution, the proposed solution can be more general and more suitable
for real-world scenarios. Experimental evaluations implemented on different
simulated traffic scenarios have shown our method to be effective and better
than other baselines by a considerable margin.
