Traditional interactive environments limit agents' intelligence growth with fixed tasks. Recently, single-agent environments address this by generating new tasks based on agent actions, enhancing task diversity. We consider the decision-making problem in multi-agent settings, where tasks are further influenced by social connections, affecting rewards and information access. However, existing multi-agent environments lack a combination of adaptive physical surroundings and social connections, hindering the learning of intelligent behaviors. To address this, we introduce AdaSociety, a customizable multi-agent environment featuring expanding state and action spaces, alongside explicit and alterable social structures. As agents progress, the environment adaptively generates new tasks with social structures for agents to undertake. In AdaSociety, we develop three mini-games showcasing distinct social structures and tasks. Initial results demonstrate that specific social structures can promote both individual and collective benefits, though current reinforcement learning and LLM-based algorithms show limited effectiveness in leveraging social structures to enhance performance. Overall, AdaSociety serves as a valuable research platform for exploring intelligence in diverse physical and social settings. The code is available at https://github.com/bigai-ai/AdaSociety.

翻译：传统交互环境通过固定任务限制了智能体的智能发展。近期，单智能体环境通过基于智能体行为生成新任务来解决这一问题，从而提升了任务多样性。我们研究了多智能体场景下的决策问题，其中任务进一步受到社交关系的影响，这些关系会改变奖励机制与信息获取途径。然而，现有多智能体环境缺乏自适应物理环境与社交结构的结合，阻碍了智能行为的学习。为此，我们提出了AdaSociety，一个可定制的多智能体环境，具备可扩展的状态与动作空间，以及显式且可调整的社交结构。随着智能体的能力发展，环境会自适应地生成具有社交结构的新任务供智能体执行。在AdaSociety中，我们开发了三个迷你游戏，分别展示了不同的社交结构与任务类型。初步实验结果表明，特定的社交结构能够同时促进个体与集体利益，但当前基于强化学习与大语言模型的算法在利用社交结构提升性能方面效果有限。总体而言，AdaSociety为探索多样化物理与社会场景中的智能行为提供了有价值的研究平台。代码已开源：https://github.com/bigai-ai/AdaSociety。