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To support popular Internet of Things (IoT) applications such as virtualreality, mobile games and wearable devices, edge computing provides a front-enddistributed computing archetype of centralized cloud computing with lowlatency. However, it's challenging for end users to offload computation due totheir massive requirements on spectrum and computation resources and frequentrequests on Radio Access Technology (RAT). In this paper, we investigatecomputation offloading mechanism with resource allocation in IoT edge computingnetworks by formulating it as a stochastic game. Here, each end user is alearning agent observing its local environment to learn optimal decisions oneither local computing or edge computing with the goal of minimizing long termsystem cost by choosing its transmit power level, RAT and sub-channel withoutknowing any information of the other end users. Therefore, a multi-agentreinforcement learning framework is developed to solve the stochastic game witha proposed independent learners based multi-agent Q-learning (IL-based MA-Q)algorithm. Simulations demonstrate that the proposed IL-based MA-Q algorithm isfeasible to solve the formulated problem and is more energy efficient withoutextra cost on channel estimation at the centralized gateway compared to theother two benchmark algorithms.

Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing