Very Large-Scale Multi-Agent Simulation in AgentScope
Xuchen Pan, Dawei Gao, Yuexiang Xie, Zhewei Wei, Yaliang Li, Bolin Ding, Ji-Rong Wen, Jingren Zhou
2024-07-26
Summary
This paper discusses AgentScope, a platform designed to help researchers simulate large numbers of virtual agents interacting in various scenarios. It introduces new features that make it easier to create and manage these simulations effectively.
What's the problem?
Current platforms for multi-agent simulations face several challenges, such as not being able to handle a large number of agents efficiently, lacking diversity among the agents, and requiring a lot of manual work to set up and manage simulations. These issues make it hard for researchers to conduct comprehensive studies and explore complex interactions in real-world situations.
What's the solution?
The authors developed several enhancements for AgentScope to tackle these problems. They introduced an actor-based distributed mechanism that allows many agents to run at the same time, improving scalability and efficiency. They also created tools that simplify the process of designing diverse agents and a web-based interface for easy monitoring and management of multiple agents across different devices. This means users can create realistic simulations more easily and effectively.
Why it matters?
These improvements are significant because they enable researchers to conduct more detailed and realistic simulations in fields like economics, social sciences, and artificial intelligence. By making it easier to study how different agents interact in various environments, AgentScope can lead to new insights and advancements in understanding complex systems.
Abstract
Recent advances in large language models (LLMs) have opened new avenues for applying multi-agent systems in very large-scale simulations. However, there remain several challenges when conducting multi-agent simulations with existing platforms, such as limited scalability and low efficiency, unsatisfied agent diversity, and effort-intensive management processes. To address these challenges, we develop several new features and components for AgentScope, a user-friendly multi-agent platform, enhancing its convenience and flexibility for supporting very large-scale multi-agent simulations. Specifically, we propose an actor-based distributed mechanism as the underlying technological infrastructure towards great scalability and high efficiency, and provide flexible environment support for simulating various real-world scenarios, which enables parallel execution of multiple agents, centralized workflow orchestration, and both inter-agent and agent-environment interactions among agents. Moreover, we integrate an easy-to-use configurable tool and an automatic background generation pipeline in AgentScope, simplifying the process of creating agents with diverse yet detailed background settings. Last but not least, we provide a web-based interface for conveniently monitoring and managing a large number of agents that might deploy across multiple devices. We conduct a comprehensive simulation to demonstrate the effectiveness of the proposed enhancements in AgentScope, and provide detailed observations and discussions to highlight the great potential of applying multi-agent systems in large-scale simulations. The source code is released on GitHub at https://github.com/modelscope/agentscope to inspire further research and development in large-scale multi-agent simulations.