标题： AgentGym：在多种环境中进化基于大语言模型的智能体 显示英文标题

标题： AgentGym: Evolving Large Language Model-based Agents across Diverse Environments

摘要： 构建能够处理多样化任务并在不同环境中自我进化的通用代理是人工智能界的一个长期目标。 大型语言模型（LLMs）因其泛化能力被认为是构建此类代理的有前途的基础。 当前的方法要么让基于LLM的代理逐步模仿专家提供的轨迹，需要人工监督，这难以扩展并限制了环境探索；要么让代理在隔离环境中探索和学习，导致专业代理，泛化能力有限。 在本文中，我们迈出了构建具有自我进化能力的通用LLM代理的第一步。 我们确定了三个关键要素：1）用于代理探索和学习的多样化环境，2）一个轨迹集，使代理具备基本能力和先验知识，3）一种有效且可扩展的进化方法。 我们提出了AgentGym，一个新框架，具有多种环境和任务，用于广泛、实时、统一格式和并发的代理探索。 AgentGym还包含一个扩展指令的数据库、一个基准套件以及跨环境的高质量轨迹。 接下来，我们提出了一种新方法AgentEvol，以研究代理在任务和环境上超越之前看到的数据的自我进化潜力。 实验结果表明，进化后的代理可以达到与最先进模型相当的结果。 我们发布了AgentGym套件，包括平台、数据集、基准、检查点和算法实现。 AgentGym套件可在https://github.com/WooooDyy/AgentGym获取。 显示英文摘要

摘要： Building generalist agents that can handle diverse tasks and evolve themselves across different environments is a long-term goal in the AI community. Large language models (LLMs) are considered a promising foundation to build such agents due to their generalized capabilities. Current approaches either have LLM-based agents imitate expert-provided trajectories step-by-step, requiring human supervision, which is hard to scale and limits environmental exploration; or they let agents explore and learn in isolated environments, resulting in specialist agents with limited generalization. In this paper, we take the first step towards building generally-capable LLM-based agents with self-evolution ability. We identify a trinity of ingredients: 1) diverse environments for agent exploration and learning, 2) a trajectory set to equip agents with basic capabilities and prior knowledge, and 3) an effective and scalable evolution method. We propose AgentGym, a new framework featuring a variety of environments and tasks for broad, real-time, uni-format, and concurrent agent exploration. AgentGym also includes a database with expanded instructions, a benchmark suite, and high-quality trajectories across environments. Next, we propose a novel method, AgentEvol, to investigate the potential of agent self-evolution beyond previously seen data across tasks and environments. Experimental results show that the evolved agents can achieve results comparable to SOTA models. We release the AgentGym suite, including the platform, dataset, benchmark, checkpoints, and algorithm implementations. The AgentGym suite is available on https://github.com/WooooDyy/AgentGym.