Tackling Cooperative Incompatibility For Zero-shot Human-ai Coordination

Abstract

Securing coordination between AI agent and teammates (human players or AI agents) in contexts involving unfamiliar humans continues to pose a significant challenge in Zero-Shot Coordination. The issue of cooperative incompatibility becomes particularly prominent when an AI agent is unsuccessful in synchronizing with certain previously unknown partners. Traditional algorithms have aimed to collaborate with partners by optimizing fixed objectives within a population, fostering diversity in strategies and behaviors. However, these techniques may lead to learning loss and an inability to cooperate with specific strategies within the population, a phenomenon named cooperative incompatibility in learning. In order to solve cooperative incompatibility in learning and effectively address the problem in the context of ZSC, we introduce the Cooperative Open-ended LEarning (COLE) framework, which formulates open-ended objectives in cooperative games with two players using perspectives of graph th

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyli2023tackling

Related papers

• Cooperative Open-ended Learning Framework For Zero-shot Coordination (2023)0.00
• Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination (2025)0.00
• Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In The Game Of Hanabi (2023)0.00
• Heterogeneous Multi-agent Zero-shot Coordination By Coevolution (2022)5.24
• "other-play" For Zero-shot Coordination (2020)0.00
• Knowpc: Knowledge-driven Programmatic Reinforcement Learning For Zero-shot Coordination (2024)0.00
• PECAN: Leveraging Policy Ensemble For Context-aware Zero-shot Human-ai Coordination (2023)2.26
• A New Formalism, Method And Open Issues For Zero-shot Coordination (2021)0.00