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In order to collaborate efficiently with unknown partners in cooperativecontrol settings, adaptation of the partners based on online experience isrequired. The rather general and widely applicable control setting, where eachcooperation partner might strive for individual goals while the control lawsand objectives of the partners are unknown, entails various challenges such asthe non-stationarity of the environment, the multi-agent credit assignmentproblem, the alter-exploration problem and the coordination problem. We proposenew, modular deep decentralized Multi-Agent Reinforcement Learning mechanismsto account for these challenges. Therefore, our method uses a time-dependentprioritization of samples, incorporates a model of the system dynamics andutilizes variable, accountability-driven learning rates and simulated,artificial experiences in order to guide the learning process. Theeffectiveness of our method is demonstrated by means of a simulated, nonlinearcooperative control task.

Deep Decentralized Reinforcement Learning for Cooperative Control