Related papers: AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution

AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution

URL: http://arxiv.org/abs/2411.03519v1
Date: Tue, 05 Nov 2024 21:54:14 GMT
Title: AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution
Authors: Zhiqiang Xie, Hao Kang, Ying Sheng, Tushar Krishna, Kayvon Fatahalian, Christos Kozyrakis,
Abstract summary: AI Metropolis is a simulation engine that improves the efficiency of LLM agent simulations by incorporating out-of-order execution scheduling. Our evaluations demonstrate that AI Metropolis achieves speedups from 1.3x to 4.15x over standard parallel simulation with global synchronization.
Score: 15.596642151634319
License:
Abstract: With more advanced natural language understanding and reasoning capabilities, large language model (LLM)-powered agents are increasingly developed in simulated environments to perform complex tasks, interact with other agents, and exhibit emergent behaviors relevant to social science and gaming. However, current multi-agent simulations frequently suffer from inefficiencies due to the limited parallelism caused by false dependencies, resulting in performance bottlenecks. In this paper, we introduce AI Metropolis, a simulation engine that improves the efficiency of LLM agent simulations by incorporating out-of-order execution scheduling. By dynamically tracking real dependencies between agents, AI Metropolis minimizes false dependencies, enhancing parallelism and enabling efficient hardware utilization. Our evaluations demonstrate that AI Metropolis achieves speedups from 1.3x to 4.15x over standard parallel simulation with global synchronization, approaching optimal performance as the number of agents increases.

Related papers

DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution [114.61347672265076]
Development of MLLMs for real-world robots is challenging due to the typically limited computation and memory capacities available on robotic platforms. We propose a Dynamic Early-Exit Framework for Robotic Vision-Language-Action Model (DeeR) that automatically adjusts the size of the activated MLLM. DeeR demonstrates significant reductions in computational costs of LLM by 5.2-6.5x and GPU memory of LLM by 2-6x without compromising performance.
arXiv Detail & Related papers (2024-11-04T18:26:08Z)
GenSim: A General Social Simulation Platform with Large Language Model based Agents [111.00666003559324]
We propose a novel large language model (LLMs)-based simulation platform called textitGenSim. Our platform supports one hundred thousand agents to better simulate large-scale populations in real-world contexts. To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform.
arXiv Detail & Related papers (2024-10-06T05:02:23Z)
BeSimulator: A Large Language Model Powered Text-based Behavior Simulator [28.112491177744783]
We introduce BeSimulator as an attempt towards behavior simulation in the context of text-based environments. BeSimulator can generalize across scenarios and achieve long-horizon complex simulation.
arXiv Detail & Related papers (2024-09-24T08:37:04Z)
Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [60.05963742334746]
We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers. Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy. We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
arXiv Detail & Related papers (2024-09-12T11:50:06Z)
CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models [25.567208505574072]
CityFlowER is an advanced simulator for efficient and realistic city-wide traffic simulation. It pre-embeds Machine Learning models within the simulator, eliminating the need for external API interactions. It offers unparalleled flexibility and efficiency, particularly in large-scale simulations.
arXiv Detail & Related papers (2024-02-09T01:19:41Z)
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
arXiv Detail & Related papers (2023-10-12T20:49:15Z)
ERMAS: Becoming Robust to Reward Function Sim-to-Real Gaps in Multi-Agent Simulations [110.72725220033983]
Epsilon-Robust Multi-Agent Simulation (ERMAS) is a framework for learning AI policies that are robust to such multiagent sim-to-real gaps. ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations. In particular, ERMAS learns tax policies that are robust to changes in agent risk aversion, improving social welfare by up to 15% in complextemporal simulations.
arXiv Detail & Related papers (2021-06-10T04:32:20Z)
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
arXiv Detail & Related papers (2021-01-17T00:29:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.