MineLand: Simulating Large-Scale Multi-Agent Interactions with Limited
  Multimodal Senses and Physical Needs
Xianhao Yu Jiaqi Fu Renjia Deng Wenjuan Han
Conventional multi-agent simulators often assume perfect information and
limitless capabilities, hindering the ecological validity of social
interactions. We propose a multi-agent Minecraft simulator, MineLand, that
bridges this gap by introducing limited multimodal senses and physical needs.
Our simulator supports up to 48 agents with limited visual, auditory, and
environmental awareness, forcing them to actively communicate and collaborate
to fulfill physical needs like food and resources. This fosters dynamic and
valid multi-agent interactions. We further introduce an AI agent framework,
Alex, inspired by multitasking theory, enabling agents to handle intricate
coordination and scheduling. Our experiments demonstrate that the simulator,
the corresponding benchmark, and the AI agent framework contribute to more
ecological and nuanced collective behavior. The source code of MineLand and
Alex is openly available at https://github.com/cocacola-lab/MineLand.