Summary of Agent-pro: Learning to Evolve Via Policy-level Reflection and Optimization, by Wenqi Zhang et al.
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization
by Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang, Yongliang Shen, Guiyang Hou, Zeqi Tan, Peng Li, Yueting Zhuang, Weiming Lu
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Agent-Pro model leverages Large Language Models (LLMs) to develop an agent capable of learning and evolving through interactions. Unlike traditional task solvers that require manual prompts, Agent-Pro utilizes a policy-level reflection and optimization process to fine-tune its behavioral policy based on past trajectories and beliefs. This approach enables the agent to continually enhance its performance in complex dynamic scenarios, such as large interactive games. The model is evaluated across two games, Blackjack and Texas Hold’em, outperforming vanilla LLMs and specialized models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new type of Large Language Model (LLM) that can learn and improve through interactions. This “agent” uses a special way to think about its past actions and adjust its behavior accordingly. It’s like a game-playing AI that gets better and better as it plays. The researchers tested this agent on two different games, Blackjack and Texas Hold’em, and found that it performed better than other types of AIs. |
Keywords
» Artificial intelligence » Large language model » Optimization
