  Traffic Engineering (TE) is an efficient technique to balance network flows
and thus improves the performance of a hybrid Software Defined Network (SDN).
Previous TE solutions mainly leverage heuristic algorithms to centrally
optimize link weight setting or traffic splitting ratios under the static
traffic demand. Note that as the network scale becomes larger and network
management gains more complexity, it is notably that the centralized TE methods
suffer from a high computation overhead and a long reaction time to optimize
routing of flows when the network traffic demand dynamically fluctuates or
network failures happen. To enable adaptive and efficient routing in TE, we
propose a Multi-agent Reinforcement Learning method CMRL that divides the
routing optimization of a large network into multiple small-scale routing
decisionmaking problems. To coordinate the multiple agents for achieving a
global optimization goal, we construct an interactive environment for training
the routing agents that own partial link utilization observations. To optimize
credit assignment of multi-agent, we introduce the difference reward assignment
mechanism for encouraging agents to take better action. Extensive simulations
conducted on the real traffic traces demonstrate the superiority of CMRL in
improving TE performance, especially when traffic demands change or network
failures happen.
