构建开放多智能体社交学习环境，研究智能体如何通过隐性合作与专家学习提升能力。
An Efficient Open World Environment for Multi-Agent Social Learning
- 设计可支持多智能体自主目标的开放世界环境，模拟真实社会场景。
- 智能体在专家引导下表现更优，隐性合作能促进工具共享与长期目标达成。
- 适合研究社交智能、协作机制与自适应行为的学习方法。
当前人工智能代理在现实环境中部署仍面临诸多挑战，而真实环境本质上具有多智能体特性且包含人类专家。利用先进的社会智能，可帮助AI代理学习专家表现出的适应性技能与行为。尽管社会智能能加速训练，但因缺乏开放式的多智能体环境而难以研究。本文提出一个环境，其中多个自利智能体可追求复杂且独立的目标，反映现实世界挑战。该环境将推动在开放式多智能体设置中发展社会智能代理的研究，使代理可能被隐性激励去合作对抗共同敌人、共建并共享工具，以及实现长时程目标。本文探究在专家存在及隐性合作（如涌现的协作工具使用）条件下，社会学习对代理性能的影响，并检验代理是否能从合作或竞争中获益。
原文摘要 · Abstract (English)
Many challenges remain before AI agents can be deployed in real-world environments. However, one virtue of such environments is that they are inherently multi-agent and contain human experts. Using advanced social intelligence in such an environment can help an AI agent learn adaptive skills and behaviors that a known expert exhibits. While social intelligence could accelerate training, it is currently difficult to study due to the lack of open-ended multi-agent environments. In this work, we present an environment in which multiple self-interested agents can pursue complex and independent goals, reflective of real world challenges. This environment will enable research into the development of socially intelligent AI agents in open-ended multi-agent settings, where agents may be implicitly incentivized to cooperate to defeat common enemies, build and share tools, and achieve long horizon goals. In this work, we investigate the impact on agent performance due to social learning in the presence of experts and implicit cooperation such as emergent collaborative tool use, and whether agents can benefit from either cooperation or competition in this environment.
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