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The recent success of single-agent reinforcement learning (RL) in Internet ofthings (IoT) systems motivates the study of multi-agent reinforcement learning(MARL), which is more challenging but more useful in large-scale IoT. In thispaper, we consider a voting-based MARL problem, in which the agents vote tomake group decisions and the goal is to maximize the globally averaged returns.To this end, we formulate the MARL problem based on the linear programming formof the policy optimization problem and propose a distributed primal-dualalgorithm to obtain the optimal solution. We also propose a voting mechanismthrough which the distributed learning achieves the same sublinear convergencerate as centralized learning. In other words, the distributed decision makingdoes not slow down the process of achieving global consensus on optimality.Lastly, we verify the convergence of our proposed algorithm with numericalsimulations and conduct case studies in practical multi-agent IoT systems.

Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT