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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

Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-02303650
Contributor : Yiping Fang <>
Submitted on : Wednesday, October 2, 2019 - 2:42:23 PM
Last modification on : Saturday, May 8, 2021 - 3:40:32 AM

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Daogui Tang, Yiping 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 (ESREL2019), Sep 2019, Hannover, Germany. ⟨10.3850/978-981-11-2724-3_0743-cd⟩. ⟨hal-02303650⟩

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