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Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Core Concepts
Agent-Pro is an LLM-based agent that learns and evolves through policy-level reflection and optimization, outperforming vanilla LLMs and specialized models in interactive games.
Agent-Pro introduces a novel approach for LLM-based agents to learn and evolve in interactive scenarios. The agent constructs dynamic beliefs for decision-making and reflects on past experiences to optimize its policy. Through policy-level reflection and optimization, Agent-Pro improves its decision-making capabilities and outperforms other models in games like Blackjack and Texas Hold’em. Agent-Pro's evolution process enhances its strategic skills and adaptability in complex and dynamic scenarios.
Agent-Pro significantly surpasses most baseline agents with an average advantage of +4%. Agent-Pro improves performance on GPT-4, GPT-3.5, and Llama2-70B, with an average score increase of +2 points. Agent-Pro outperforms RL-based agents and other LLM-based agents across various games.
"Agent-Pro can learn and evolve in complex and dynamic scenes, benefiting numerous LLM-based applications." "Our results show Agent-Pro can defeat vanilla LLMs and specialized models, improving the game’s payoffs."

Key Insights Distilled From

by Wenqi Zhang,... at 03-28-2024

Deeper Inquiries

What are the potential limitations of Agent-Pro's dependency on the foundational model for its learning process

One potential limitation of Agent-Pro's dependency on the foundational model for its learning process is the scalability and generalization of the approach. Since Agent-Pro heavily relies on the reasoning and reflection abilities of the foundational model, the performance of Agent-Pro is directly impacted by the capabilities of the underlying model. If the foundational model lacks certain reasoning or reflection capabilities, it may hinder the learning and evolution process of Agent-Pro. This dependency could limit the applicability of Agent-Pro to scenarios where the foundational model may not be as robust or effective. Additionally, the need for a strong foundational model may restrict the deployment of Agent-Pro in environments where access to such models is limited or not feasible.

How does Agent-Pro's performance compare to state-of-the-art algorithms in gaming scenarios

In comparison to state-of-the-art algorithms in gaming scenarios, Agent-Pro demonstrates significant progress and outperforms many baseline models. The performance of Agent-Pro in gaming scenarios, especially in games like Blackjack and Limit Texas Hold’em, showcases notable improvements in decision-making capabilities and strategic adaptability. While there may still be a gap between Agent-Pro and advanced algorithms like CFR plus, Agent-Pro's ability to learn and evolve within interactive environments sets it apart from traditional models. By leveraging dynamic beliefs, policy-level reflection, and prompt optimization, Agent-Pro can enhance its performance and adaptability in complex and dynamic gaming scenarios.

How can Agent-Pro's evolution process be further optimized to bridge the gap with other advanced algorithms

To further optimize Agent-Pro's evolution process and bridge the gap with other advanced algorithms, several strategies can be implemented. Firstly, enhancing the learning capabilities of the foundational model through continuous training and fine-tuning can improve the overall performance of Agent-Pro. Additionally, incorporating more advanced techniques such as meta-learning or reinforcement learning can help Agent-Pro adapt to a wider range of scenarios and improve its decision-making abilities. Furthermore, exploring ensemble methods or hybrid approaches that combine the strengths of different algorithms can potentially boost the performance of Agent-Pro and enable it to compete more effectively with state-of-the-art algorithms in gaming scenarios.