We present a novel multi-agent simulator named Multi-Agent eXperimenter (MAX)
that is designed to simulate blockchain experiments involving large numbers of
agents of different types acting in one or several environments. The
architecture of MAX is highly modular, enabling easy addition of new models.

我们提出了一种名为 Multi-Agent eXperimenter (MAX) 的新型多代理模拟器，旨在模拟涉及大量不同类型代理在一个或多个环境中行动的区块链实验。MAX 的体系结构高度模块化，可以方便地添加新模型。

多智能体实验者 (MAX)

Multi-Agent eXperimenter (MAX)

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 this https URL

我们提出了一种多智能体 Minecraft 模拟器 MineLand，通过引入有限的多模态感知和物质需求，弥合了传统多智能体模拟器对完美信息和无限能力的假设，支持最多 48 个智能体，强制它们通过通信和协作来满足食物和资源等物质需求，从而促进了动态有效的多智能体交互，还引入了一个灵感来自多任务理论的 AI 智能体框架 Alex，使智能体能够处理复杂的协调和调度，实验证明该模拟器、相应的基准测试和 AI 智能体框架对于实现更生态和细致的集体行为具有积极作用。

MineLand: 模拟大规模多智能体交互，具有有限的多模态感知和生理需求

MineLand: Simulating Large-Scale Multi-Agent Interactions with Limited  Multimodal Senses and Physical Needs

We show how a multi-agent simulator can support two important but distinct
methods for assessing a trading strategy: Market Replay and Interactive
Agent-Based Simulation (IABS). Our solution is important because each method
offers strengths and weaknesses that expose or conceal flaws in the subject
strategy. A key weakness of Market Replay is that the simulated market does not
substantially adapt to or respond to the presence of the experimental strategy.
IABS methods provide an artificial market for the experimental strategy using a
population of background trading agents. Because the background agents attend
to market conditions and current price as part of their strategy, the overall
market is responsive to the presence of the experimental strategy. Even so,
IABS methods have their own weaknesses, primarily that it is unclear if the
market environment they provide is realistic. We describe our approach in
detail, and illustrate its use in an example application: The evaluation of
market impact for various size orders.

我们描述了一种使用多智能体模拟器的方法，支持评估交易策略的两种不同方法：市场回放和交互式基于代理的模拟（IABS）。我们的方法很重要，因为每种方法都具有优点和缺点，可以揭示或隐藏主题战略中的缺陷。我们在一个示例应用程序中说明了它的用途：评估不同大小订单的市场影响。