From Limited Tasks to General AI: AGENTGYM Evolves Agents with Diverse Environments and Autonomous Learning

From Limited Tasks to General AI: AGENTGYM Evolves Agents with Diverse Environments and Autonomous Learning

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Artificial intelligence (AI) research aims to develop agents capable of performing diverse tasks autonomously. The goal is to create versatile AI systems that can handle real-world challenges.

Challenges in AI

A significant challenge in AI is creating agents that can learn and adapt independently across different tasks and environments without extensive human intervention.

Research and Frameworks

Existing research includes frameworks like AgentBench, AgentBoard, and AgentOhana, which focus on evaluating and developing large language model-based agents. Other significant contributions include the AGENTGYM framework by Fudan NLP Lab & Fudan Vision and Learning Lab.

AGENTGYM Framework

The AGENTGYM framework supports diverse environments and tasks, enabling agents to explore broadly and in real time. It provides a comprehensive suite of tools and environments for training and evaluating large language model-based agents, aiming to enhance their adaptability and performance.


The AGENTGYM framework employs the AGENTEVOL method, which allows agents to evolve by interacting with different environments and learning from new experiences. This enhances the agents’ ability to generalize and adapt to new tasks.

Experimental Results

Experimental results demonstrate that agents evolved using AGENTEVOL perform comparably to state-of-the-art models across various tasks, showcasing significant improvement in their ability to generalize and adapt to new tasks and environments.


The AGENTGYM framework represents a significant stride in creating generally-capable AI agents, overcoming key limitations of current methods. The promising results demonstrate the potential of integrating diverse environments and autonomous learning methods to create more capable and generalist AI agents.

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