Targets in Reinforcement Learning to solve Stackelberg Security Games
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Abstract (arXiv)
Reinforcement Learning (RL) algorithms have been successfully applied to real world situations like illegal smuggling, poaching, deforestation, climate change, airport security, etc. These scenarios can be framed as Stackelberg security games (SSGs) where defenders and attackers compete to control target resources. The algorithm's competency is assessed by which agent is controlling the targets. This review investigates modeling of SSGs in RL with a focus on possible improvements of target representations in RL algorithms.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 27 references that are in the map. First posted on arXiv 30 Nov 2022.
Builds on
8 of 27 references.
References · 8
- 2013The Complexity of Decentralized Control of Markov Decision Processescited by 231
- 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 220
- 2021Evaluating the Robustness of Collaborative Agentscited by 25
- 2018Deep Reinforcement Learning for Green Security Games with Real-Time Informationcited by 19
- 2020Model-free Reinforcement Learning for Stochastic Stackelberg Security Gamescited by 4
- 2021Single-Leader-Multiple-Followers Stackelberg Security Game with Hypergame Frameworkcited by 4
- 2018On the Inducibility of Stackelberg Equilibrium for Security Gamescited by 3
- 2020Reinforcement Learning for Unified Allocation and Patrolling in Signaling Games with Uncertaintycited by 2
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