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A zero-sum Markov defender-attacker game for modeling false pricing in smart grids and its solution by multi-agent reinforcement learning

Résumé

Consumers in smart grids are expected to engage demand-response programs by two-way communication. This makes smart grids vulnerable to cyber attacks. In this paper, we study the false pricing attacks and model the interaction between attackers and defenders using a zero-sum Markov game, where neither player has full knowledge of the game model. A multi-agent reinforcement learning method is used to solve the Markov game and find the Nash Equilibrium policies for both players. An application to a simple radial power distribution system is worked out. The results show that the proposed algorithm can help the players find mixed strategies to maximize their long-term return. Copyright
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Dates et versions

hal-04317737 , version 1 (01-12-2023)

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Daogui Tang, Yi-Ping Fang, Enrico Zio. A zero-sum Markov defender-attacker game for modeling false pricing in smart grids and its solution by multi-agent reinforcement learning. 29th European Safety and Reliability Conference, ESREL 2019, 2019, Hannover, Germany. pp.3285-3291, ⟨10.3850/978-981-11-2724-30743-cd⟩. ⟨hal-04317737⟩
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