Adversarial Attacks In Cooperative AI

Abstract

Single-agent reinforcement learning algorithms in a multi-agent environment are inadequate for fostering cooperation. If intelligent agents are to interact and work together to solve complex problems, methods that counter non-cooperative behavior are needed to facilitate the training of multiple agents. This is the goal of cooperative AI. Recent research in adversarial machine learning, however, shows that models (e.g., image classifiers) can be easily deceived into making inferior decisions. Meanwhile, an important line of research in cooperative AI has focused on introducing algorithmic improvements that accelerate learning of optimally cooperative behavior. We argue that prominent methods of cooperative AI are exposed to weaknesses analogous to those studied in prior machine learning research. More specifically, we show that three algorithms inspired by human-like social intelligence are, in principle, vulnerable to attacks that exploit weaknesses introduced by cooperative AI's algo

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyfujimoto2021adversarial

Related papers

• Constrained Black-box Attacks Against Cooperative Multi-agent Reinforcement Learning (2025)0.00
• Adversarial Attacks In Consensus-based Multi-agent Reinforcement Learning (2021)0.00
• Cooperative Artificial Intelligence (2022)0.00
• Attacking Cooperative Multi-agent Reinforcement Learning By Adversarial Minority Influence (2023)0.00
• Multi-task Federated Reinforcement Learning With Adversaries (2021)0.00
• Collaboration Of AI Agents Via Cooperative Multi-agent Deep Reinforcement Learning (2019)0.00
• Independent Generative Adversarial Self-imitation Learning In Cooperative Multiagent Systems (2019)0.00
• Cooperative And Competitive Biases For Multi-agent Reinforcement Learning (2021)2.26