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Multi-agent reinforcement learning has been paid much attention due to its wide applications in various engineering systems. In this paper, the control problems of large-scale multi-agent systems with multiple roles are formulated into a multiplayer Stackelberg game, which provides a new perspective on cooperative issues. Then a Stackelberg Q-learning algorithm is proposed and knowledge transfer is...
The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of a value-updating algorithm...
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