Steering No-Regret Learners to a Desired Equilibrium
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Abstract (arXiv)
A mediator observes no-regret learners playing an extensive-form game repeatedly across T rounds. The mediator attempts to steer players toward some desirable predetermined equilibrium by giving (nonnegative) payments to players. We call this the steering problem. The steering problem captures problems several problems of interest, among them equilibrium selection and information design (persuasion). If the mediator's budget is unbounded, steering is trivial because the mediator can simply pay the players to play desirable actions. We study two bounds on the mediator's payments: a total budget and a per-round budget. If the mediator's total budget does not grow with T, we show that steering is impossible. However, we show that it is enough for the total budget to grow sublinearly with T, that is, for the average payment to vanish. When players' full strategies are observed at each round, we show that constant per-round budgets permit steering. In the more challenging setting where only trajectories through the game tree are observable, we show that steering is impossible with constant per-round budgets in general extensive-form games, but possible in normal-form games or if the per-round budget may itself depend on T. We also show how our results can be generalized to the case when the equilibrium is being computed online while steering is happening. We supplement our theoretical positive results with experiments highlighting the efficacy of steering in large games.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jun 2023.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 2.
6 papers.
- 2024Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contractscited by 9
- 2025LLM-Mediated Guidance of MARL Systemscited by 3
- 2025Steering No-Regret Agents in MFGs under Model Uncertaintycited by 1
- 2025A Soft Inducement Framework for Incentive-Aided Steering of No-Regret Playerscited by 1
- 2026Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Playcited by 0
- 2026Equilibrium with Internal Transferscited by 0
- 2026Equilibrium with Internal Transfers18 Jun 2026
- 2026Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Play17 Jun 2026
- 2025A Soft Inducement Framework for Incentive-Aided Steering of No-Regret Players29 Aug 2025
- 2025LLM-Mediated Guidance of MARL Systems16 Mar 2025
- 2025Steering No-Regret Agents in MFGs under Model Uncertainty12 Mar 2025
- 2024Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts25 Jul 2024