Network load balancing strategy based on supervised reinforcement learning with shaping rewards

This paper proposes supervised reinforcement learning (SRL) algorithm for network load balancing strategy with shaping rewards. We define the index of router as state set; design additional distance improving reward and load balancing reward to construct the supervisor; adopt epsilon greedy algorithm as the action selecting strategy and prove that the state transmission is a deterministic matrix. Besides, we carry out the simulation work which demonstrates that by maximizing the sum of discounted rewards, SRL is an effective controller for network load balancing strategy; each router can apply this algorithm to calculate the optimal path to other routers with network load balancing requirement.