Optimally Installing Strict Equilibria
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Abstract (arXiv)
In this work, we develop a reward design framework for installing a desired behavior as a strict equilibrium across standard solution concepts: dominant strategy equilibrium, Nash equilibrium, correlated equilibrium, and coarse correlated equilibrium. We also extend our framework to capture the Markov-perfect equivalents of each solution concept. Central to our framework is a comprehensive mathematical characterization of strictly installable, based on the desired solution concept and the behavior's structure. These characterizations lead to efficient iterative algorithms, which we generalize to handle optimization objectives through linear programming. Finally, we explore how our results generalize to bounded rational agents.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 5 Mar 2025.
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References · 11
- 2020Adaptive Reward-Poisoning Attacks against Reinforcement Learningcited by 86
- 2020Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learningcited by 81
- 2019Policy Poisoning in Batch Reinforcement Learning and Controlcited by 70
- 2019Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signalscited by 50
- 2021Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environmentscited by 26
- 2020Policy Teaching in Reinforcement Learning via Environment Poisoning Attackscited by 24
- 2022Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learningcited by 21
- 2022Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learningcited by 20
- 2022Admissible Policy Teaching through Reward Designcited by 12
- 2021Game Redesign in No-regret Game Playingcited by 8
- 2023Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Valuecited by 3
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