The Goal of AI Research

Develop agents capable of various tasks across diverse environments.Achieve human like learning and adaptability.Create versatile AI systems for real-world applications.

Challenges in General AI

Limited scalability and adaptability due to extensive human intervention. Difficulty in developing autonomous systems that learn and improve independently.

Existing Research in AI Agents

Frameworks like AgentBench, AgentBoard, and AgentOhana focus on large language model-based agents. These frameworks use methods like behavioral cloning and isolated environment training, limiting scalability and generalization.

Limitations of Existing Research

Limited scalability and generalizability. Requires a lot of human intervention.

Introducing AGENTGYM: A New Framework for Evolving AI

Developed by Fudan NLP Lab & Fudan Vision and Learning Lab. Supports diverse environments and tasks for broad, real-time exploration.Provides a comprehensive suite of tools for training and evaluating LLM agents.

AGENTEVOL: Empowering Agent Evolution

AGENTEVOL: a revolutionary method for agent evolution. Enables agents to learn and adapt through interaction with diverse environments. Enhances generalization and ability to tackle new tasks.

AGENTGYM's Comprehensive Toolkit

 Facilitating their evolution and generalization across tasks. Designed to enhance adaptability and performance through a robust training environment.

AGENTEVOL: Powering Up Agent Evolution

AGENTEVOL is a method within AGENTGYM that allows agents to evolve through interaction with diverse environments. As agents encounter new experiences, they learn and adapt, improving their ability to generalize.