Adversarial Team Game

Adversarial team games analyze strategic interactions where a team of cooperating agents competes against an opposing team, often within a Markov game framework. Current research focuses on developing robust algorithms, such as actor-critic methods and linear programming approaches, to compute equilibrium strategies under conditions of uncertainty and adversarial state perturbations. This field is crucial for improving the robustness of multi-agent reinforcement learning systems in real-world applications, particularly in areas like cybersecurity and autonomous systems, where agents must function reliably despite adversarial actions. The development of efficient algorithms and new solution concepts, like robust agent policies, addresses the challenges posed by adaptive adversaries and aims to enhance the security and reliability of AI systems.

Papers

October 24, 2024

SAMG: State-Action-Aware Offline-to-Online Reinforcement Learning with Offline Model Guidance
Liyu Zhang, Haochi Wu, Xu Wan, Quan Kong, Ruilong Deng, Mingyang Sun
Reinforcement Learning Reinforcement Learning Algorithm Offline Policy Adversarial Team Game

December 20, 2023

Adversarial Markov Games: On Adaptive Decision-Based Attacks and Defenses
Ilias Tsingenopoulos, Vera Rimmer, Davy Preuveneers, Fabio Pierazzi, Lorenzo Cavallaro, Wouter Joosen
Adaptive Importance Adaptive Attack Adaptive Adversary Multi Step Adversarial Attack Decision Based Adaptive Defense Adversarial Team Game

December 6, 2022

What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?
Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, Shaofeng Zou, Fei Miao
Multi Agent Reinforcement Learning Solution Path Robust Policy Sub Optimal Robust Agent Adversarial Multi Adversarial Team Game

August 3, 2022

Efficiently Computing Nash Equilibria in Adversarial Team Markov Games
Fivos Kalogiannis, Ioannis Anagnostides, Ioannis Panageas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Vaggos Chatziafratis, Stelios Stavroulakis
Nash Equilibrium Markov Game Adversarial Team Game

June 18, 2022

A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
Luca Carminati, Federico Cacciamani, Marco Ciccone, Nicola Gatti
Video Game Zero Sum Game Regret Learning Equilibrium Computation Adversarial Team Game

May 15, 2022

RoMFAC: A robust mean-field actor-critic reinforcement learning against adversarial perturbations on states
Ziyuan Zhou, Guanjun Liu
Multi Agent Adversarial Perturbation Policy Gradient Multi Agent Deep Reinforcement Learning Actor Critic Reinforcement Learning State Specific Decision Making Mean Field Reinforcement Learning Adversarial Team Game

January 25, 2022

Public Information Representation for Adversarial Team Games
Luca Carminati, Federico Cacciamani, Marco Ciccone, Nicola Gatti
Available Datasets Zero Sum Game Two Player Zero Sum Adversarial Team Game