Stackelberg Games

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Stackelberg games model sequential strategic decisions: a leader chooses a policy or commitment while anticipating how followers will respond. Researchers analyze equilibria and develop learning methods including regret minimization, calibration, bilevel optimization, and reinforcement learning, with applications in security, markets, networks, and multi-agent control. Work also studies how to learn when follower behavior or rewards are unknown, using structured complexity bounds, active exploration, and benchmarks for adaptive opponents. A central challenge is making guarantees computationally tractable and reliable when responses are uncertain, changing, or more complex than the assumed model.

Latest papers

19 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Learning Rewards in Strategic Games
Oct 15, 2026Antoine Bergerault, Cyrille Kone, Giorgia RamponiReinforcement Learning Multi-Agent Reinforcement Learning
ALPI Lab, University of Zurich
Can You Trust Your Preference Models? Stackelberg Games for Alignment
Oct 10, 2026Riccardo Formenti, Aditya Agrawal, Vikas GargLLM Alignment Preference-Based RL
Department of Mathematics ETH Zürich · Department of Computer Science Aalto University · YaiYai Ltd and Aalto University
Defensive Sufficiency in a Stackelberg Model of AI Security
Oct 7, 2026Subhabrata Majumdar, Rajlakshmi ChavanCybersecurity Stackelberg Games
Indian Institute of Management Bangalore
Learnable Randomization as Commitment Against Adaptive Optimizers
Sep 26, 2026Zihan Deng, Chuanzhi Xu, Xiaozhen Zhong +2Policy Optimization Stackelberg Games
The University of Hong Kong · The University of Sydney · University of Electronic Science and Technology of China +1
Decision-Focused Learning in Network Interdiction Games
Aug 10, 2026Luca M. Hartmann, Parinaz NaghizadehAdversarial Training Stackelberg Games
ECE Department, UC San Diego
CoNav-UAV: Cooperative Dual-Altitude Aerial Navigation via Stackelberg Learning
Aug 3, 2026Junru Song, Wenhao Zhang, Yang Yang +7UAV Navigation Stackelberg Games
1Shanghai Jiao Tong University · 2Intelligent Game and Decision Laboratory · 3Renmin University of China +1
Entropy-Regularized Reinforcement Learning for Linear-Quadratic Stackelberg Differential Games in Regime-Switching Diffusion Models
Jun 27, 2026Congde Hu, Danping Li, Lin Xu +1Stackelberg Games Maximum Entropy RL
School of Mathematics and Statistics, Anhui Normal University, Wuhu, Anhui 241002, China · School of Statistics, Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, Faculty of Economics and Management, East China Normal University, Shanghai, China · School of Mathematics, Southeast University, Nanjing, Jiangsu 211189, China
A Stackelberg Framework for Resource-Aware LLM Agents: Learning, Repair, and Conditional Guarantees
Jun 22, 2026Baoxun WangStackelberg Games AI Agent Governance
Platform and Content Group, Tencent
Learning from Acceptance: Cumulative Regret in the Game of Coding
May 10, 2026Hanzaleh Akbari Nodehi, Parsa Moradi, Mohammad Ali Maddah-AliImperfect-Information Games Game Theory
University of Minnesota, Twin Cities, Minneapolis, MN, USA
Rethinking Priority Scheduling for Sequential Multi-Agent Decision Making in Stackelberg Games
May 8, 2026Xiangyu Liu, Liang Zhang, Bo Jin +1Sequential Decision Making Hierarchical RL
Dalian University of Technology
A Benchmark for Strategic Auditee Gaming Under Continuous Compliance Monitoring
May 7, 2026Florian A. D. Burnat, Brittany I. DavidsonAlgorithmic Auditing Stackelberg Games
University of Bath, Bath, England, UK.
Finite-Time Analysis of Q-Value Iteration for General-Sum Stackelberg Games
Apr 6, 2026Narim Jeong, Donghwan LeeStackelberg Games Game-Theoretic RL
Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
Evolutionarily Stable Stackelberg Equilibrium
Mar 19, 2026Sam GanzfriedGame Theory Stackelberg Games
Ganzfried Research, Cornell University
Understanding Strategic Platform Entry and Seller Exploration: A Stackelberg Model
Mar 15, 2026Garrett Seo, Xintong Wang, David C. ParkesE-Commerce Stackelberg Games
Rutgers University, New Brunswick, NJ, USA · Harvard University, Cambridge, MA, USA
Strategic Interactions in Multi-Level Stackelberg Games with Non-Follower Agents and Heterogeneous Leaders
Mar 4, 2026Niloofar Aminikalibar, Farzaneh Farhadi, Maria ChliStackelberg Games
Aston University, Birmingham, United Kingdom
Zeroth-Order Stackelberg Control in Combinatorial Congestion Games
Feb 26, 2026Saeed Masiha, Sepehr Elahi, Negar Kiyavash +1Frank-Wolfe Algorithms Stackelberg Games
EPFL School of Management of Technology · EPFL Department of Computer and Communications Sciences
LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach
Oct 13, 2025Renxuan Tan, Rongpeng Li, Fei Wang +4Agentic RL Stackelberg Games
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China · Huawei Technologies Company Ltd., Shanghai 210026, China · Zhejiang Lab as well as Zhejiang University, Hangzhou 310027, China +1
Learning in Structured Stackelberg Games
Apr 11, 2025Maria-Florina Balcan, Kiriaki Fragkia, Keegan HarrisGame Theory Stackelberg Games
Carnegie Mellon University, Pittsburgh, USA · University of California, Berkeley, USA
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
Jun 5, 2023Nika Haghtalab, Chara Podimata, Kunhe YangImperfect-Information Games Game Theory
University of California, Berkeley · Massachusetts Institute of Technology

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Learning Rewards in Strategic Games

Antoine Bergerault, Cyrille Kone, Giorgia Ramponi

ALPI Lab, University of Zurich

Reinforcement Learning Multi-Agent Reinforcement Learning Stackelberg Games

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Understanding the objectives that drive agents' behavior is crucial in strategic environments, where multiple agents interact and adapt to one another. This challenge arises in domains such as cybersecurity, autonomous driving, and online markets, where agents' underlying incentives are typically unknown and must be inferred from their observed behavior. Inverse reinforcement learning (IRL) provides a natural framework for this problem, but extending IRL to strategic multi-agent settings introduces a fundamental challenge: a reward that explains observed behavior does not necessarily induce optimal strategies when used for decision-making. In this work, we study this problem through the lens of strategic identifiability, and show that learning from a fixed policy pair can be insufficient to strategically identify rewards, independent of the number of expert samples

K

. Motivated by this, we introduce an active IRL algorithm for two-player repeated Stackelberg games, in which the leader adaptively selects policies to elicit informative responses about the follower's reward. Combining maximum-likelihood estimation with experimental design, we derive finite-sample guarantees on the Stackelberg sub-optimality induced by the learned reward, matching up to polylogarithmic factors a new minimax learning rate of

K^{-1/2}

. Finally, we evaluate our approach in a realistic anti-poaching setting, demonstrating the benefits of active exploration for reward recovery and downstream strategic performance.