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Xiyun Li

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4 papers

2 author rows

ICML Conference 2025 Conference Paper

Cradle: Empowering Foundation Agents towards General Computer Control

Weihao Tan
Wentao Zhang 0007
Xinrun Xu
Haochong Xia
Ziluo Ding
Boyu Li 0003
Bohan Zhou
Junpeng Yue

Despite their success in specific scenarios, existing foundation agents still struggle to generalize across various virtual scenarios, mainly due to the dramatically different encapsulations of environments with manually designed observation and action spaces. To handle this issue, we propose the General Computer Control (GCC) setting to restrict foundation agents to interact with software through the most unified and standardized interface, i. e. , using screenshots as input and keyboard and mouse actions as output. We introduce Cradle, a modular and flexible LMM-powered framework, as a preliminary attempt towards GCC. Enhanced by six key modules, Information Gathering, Self-Reflection, Task Inference, Skill Curation, Action Planning, and Memory, Cradle is able to understand input screenshots and output executable code for low-level keyboard and mouse control after high-level planning and information retrieval, so that Cradle can interact with any software and complete long-horizon complex tasks without relying on any built-in APIs. Experimental results show that Cradle exhibits remarkable generalizability and impressive performance across four previously unexplored commercial video games (Red Dead Redemption 2, Cities: Skylines, Stardew Valley and Dealer’s Life 2), five software applications (Chrome, Outlook, Feishu, Meitu and CapCut), and a comprehensive benchmark, OSWorld. With a unified interface to interact with any software, Cradle greatly extends the reach of foundation agents thus paving the way for generalist agents.

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NeurIPS Conference 2025 Conference Paper

DAIL: Beyond Task Ambiguity for Language-Conditioned Reinforcement Learning

Runpeng Xie
Quanwei Wang
Hao Hu
Zherui Zhou
Ni Mu
Xiyun Li
Yiqin Yang
Shuang Xu

Comprehending natural language and following human instructions are critical capabilities for intelligent agents. However, the flexibility of linguistic instructions induces substantial ambiguity across language-conditioned tasks, severely degrading algorithmic performance. To address these limitations, we present a novel method named DAIL (Distributional Aligned Learning), featuring two key components: distributional policy and semantic alignment. Specifically, we provide theoretical results that the value distribution estimation mechanism enhances task differentiability. Meanwhile, the semantic alignment module captures the correspondence between trajectories and linguistic instructions. Extensive experimental results on both structured and visual observation benchmarks demonstrate that DAIL effectively resolves instruction ambiguities, achieving superior performance to baseline methods. Our implementation is available at https: //github. com/RunpengXie/Distributional-Aligned-Learning.

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AAMAS Conference 2023 Conference Paper

M3: Modularization for Multi-task and Multi-agent Offline Pre-training

Linghui Meng
Jingqing Ruan
Xuantang Xiong
Xiyun Li
Xi Zhang
Dengpeng Xing
Bo Xu

Learning a multi-task policy is crucial in multi-agent reinforcement learning (MARL). Recent work has focused on learning in the context of online multi-task reinforcement learning, where a policy is jointly trained from scratch, aiming to generalize well to few-shot or even zero-shot tasks. However, existing online methods require tremendous interactions and are therefore unsuitable for environments where interactions are expensive. In this work, we novelly introduce the modularization for multi-task and multi-agent offline pre-training (M3) to learn high-level transferable policy representations. We claim that the discrete policy representation is critical for multi-task offline learning and accordingly leverage contexts as a task prompt to enhance the adaptability of pre-trained models to various tasks. To disentangle multiple agents of variation under heterogeneous and non-stationary properties even though they receive the same task, we employ an agent-invariant VQ-VAE to identify each of the multiple agents. We encapsulate the pretrained model as part of an online MARL algorithm and fine-tune it * These authors contribute equally to this work. † Corresponding authors. Proc. of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), A. Ricci, W. Yeoh, N. Agmon, B. An (eds.), May 29 – June 2, 2023, London, United Kingdom. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). All rights reserved. to improve generalization. We also theoretically analyze the generalization error of our method. We test the proposed method on the challenging StarCraft Multi-Agent Challenge (SMAC) tasks, and empirical results show that it can achieve supreme performance in few-shot or even zero-shot settings across multiple tasks over state-of-the-art MARL methods.

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AAMAS Conference 2022 Conference Paper

GCS: Graph-Based Coordination Strategy for Multi-Agent Reinforcement Learning

Jingqing Ruan
Yali Du
Xuantang Xiong
Dengpeng Xing
Xiyun Li
Linghui Meng
Haifeng Zhang
Jun Wang

Many real-world scenarios involve a team of agents that have to coordinate their policies to achieve a shared goal. Previous studies mainly focus on decentralized control to maximize a common reward and barely consider the coordination among control policies, which is critical in dynamic and complicated environments. In this work, we propose factorizing the joint team policy into a graph generator and graph-based coordinated policy to enable coordinated behaviours among agents. The graph generator adopts an encoder-decoder framework that outputs directed acyclic graphs (DAGs) to capture the underlying dynamic decision structure. We also apply the DAGness-constrained and DAG depth-constrained optimization in the graph generator to balance efficiency and performance. The graph-based coordinated policy exploits the generated decision structure. The graph generator and coordinated policy are trained simultaneously to maximize the discounted return. Empirical evaluations on Collaborative Gaussian Squeeze, Cooperative Navigation, and Google Research Football demonstrate the superiority of the proposed method. The code is available at https: //github. com/Amanda-1997/GCS_aamas337.

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