# Optimally Installing Strict Equilibria

arXiv: https://arxiv.org/abs/2503.03676 · HTML: https://mlsanity.com/paper/2503.03676/

## 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.

## Builds on (most cited first, 11 of 11)

- [Adaptive Reward-Poisoning Attacks against Reinforcement Learning](https://mlsanity.com/paper/2003.12613/) (2020)
- [Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning](https://mlsanity.com/paper/2003.12909/) (2020)
- [Policy Poisoning in Batch Reinforcement Learning and Control](https://mlsanity.com/paper/1910.05821/) (2019)
- [Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals](https://mlsanity.com/paper/1906.10571/) (2019)
- [Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments](https://mlsanity.com/paper/2102.08492/) (2021)
- [Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks](https://mlsanity.com/paper/2011.10824/) (2020)
- [Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning](https://mlsanity.com/paper/2208.13663/) (2022)
- [Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2206.01888/) (2022)
- [Admissible Policy Teaching through Reward Design](https://mlsanity.com/paper/2201.02185/) (2022)
- [Game Redesign in No-regret Game Playing](https://mlsanity.com/paper/2110.11763/) (2021)
- [Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value](https://mlsanity.com/paper/2311.00582/) (2023)

## 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.