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With the continuous growth in communication network complexity and trafficvolume, communication load balancing solutions are receiving increasingattention. Specifically, reinforcement learning (RL)-based methods have shownimpressive performance compared with traditional rule-based methods. However,standard RL methods generally require an enormous amount of data to train, andgeneralize poorly to scenarios that are not encountered during training. Wepropose a policy reuse framework in which a policy selector chooses the mostsuitable pre-trained RL policy to execute based on the current trafficcondition. Our method hinges on a policy bank composed of policies trained on adiverse set of traffic scenarios. When deploying to an unknown trafficscenario, we select a policy from the policy bank based on the similaritybetween the previous-day traffic of the current scenario and the trafficobserved during training. Experiments demonstrate that this framework canoutperform classical and adaptive rule-based methods by a large margin.

Policy Reuse for Communication Load Balancing in Unseen Traffic Scenarios