AgentGym: Evolving Large Language Model-based Agents across Diverse Environments (2406.04151v1)

Abstract: Building generalist agents that can handle diverse tasks and evolve themselves across different environments is a long-term goal in the AI community. 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.

An Insightful Overview of "AgentGym: Evolving LLM-based Agents across Diverse Environments"

Introduction

The paper "AgentGym: Evolving LLM-based Agents across Diverse Environments" introduces a novel framework aimed at advancing the development of LLM-based agents. The primary motivation behind the research is to create generalist agents capable of self-evolution, learning dynamically across varied tasks and environments without relying heavily on human supervision. This work addresses significant limitations in existing methodologies—either heavily supervised imitation learning or isolated environment exploration—by proposing a hybrid approach that emphasizes broad learning across diverse environments and an innovative evolution method.

Core Contributions

The research comprises three main contributions:

1. AgentGym Framework:
   • The authors present AgentGym, a robust platform featuring 14 environment types and 89 tasks. The platform supports interactive and real-time agent training and evaluation via convenient APIs. Tasks cover web navigation, text games, household and digital tasks, and various other domains.
   • Additionally, the platform's architecture is scalable, allowing easy integration of new environments and tasks, thus providing a comprehensive testbed for developing generally capable agents.
2. AgentEval Benchmark Suite and Trajectory Sets:
   • The team curated an expansive set of instructions (~20,509), which were filtered into a diverse benchmark suite named AgentEval comprising 1160 test cases. These were selected to ensure a comprehensive challenge for the agents.
   • Two trajectory datasets, AgentTraj and AgentTraj-L, were created. AgentTraj is used for initial agent training, while AgentTraj-L, which is larger, represents the optimal performance achievable through behavioral cloning.
3. AgentEvol Algorithm:
   • AgentEvol is a new algorithm formulated to enable agent self-evolution. It adopts a reinforcement learning-inspired approach, enhancing the agent's policy iteratively through exploration and learning steps.
   • The method leverages a variational approach to estimate optimal policies from trajectories, ensuring scalable and stable learning across diverse and previously unseen tasks.

Experimental Results

Empirical evaluation underlines the effectiveness of the proposed framework and algorithm. Some key findings include:

• Performance: Agents evolved via AgentEvol surpass those trained solely through behavioral cloning (even using the larger AgentTraj-L) and often outperform SOTA models such as GPT-4-Turbo in several tasks.
• Efficiency: The evolved agents not only achieve higher success rates but also require fewer interaction rounds, indicating better comprehension and efficiency in task execution.
• Scalability: The approach demonstrates effective handling of broad and complex task sets, suggesting the potential for developing genuinely generalist agents.

Implications and Future Directions

The practical implications of this research are substantial. The ability to train generally capable agents that exhibit autonomous evolution across diverse environments paves the way for more adaptive and robust AI systems. Such agents could be utilized in various real-world applications where dynamic learning and adaptability are critical.

Theoretically, this research contributes a novel perspective on combining imitation learning and self-learning strategies within a cohesive framework, leveraging the strengths of both worlds. The adoption of probabilistic inference techniques for policy optimization presents an interesting avenue for further exploration, especially in multi-task and multi-environment settings.

Future Directions:

• Scaling to Larger Models: Testing the evolution method on more powerful base models could yield significant insights into the upper bounds of agent capabilities.
• Safety and Alignment: Ensuring that the evolution maintains alignment with human values is crucial. Thus, integrating robust safety mechanisms into the evolution process will be an important direction.
• Expanding the Framework: Adding more environments and tasks will further test the scalability and generalization of the framework, facilitating the development of better generalist agents.

Conclusion

This paper represents a notable advancement in AI research, offering a versatile and powerful framework for developing generally capable, evolving agents. With its innovative blend of imitation learning, dynamic exploration, and scalable evolution strategies, AgentGym and the AgentEvol method set a new standard for future research and application in the domain of intelligent agents.