Agents of Change: Self-Evolving LLM Agents for Strategic Planning
Recent advances in LLMs have enabled their use as autonomous agents across a
range of tasks, yet they continue to struggle with formulating and adhering to
coherent long-term strategies. In this paper, we investigate whether LLM agents
can self-improve when placed in environments that explicitly challenge their
strategic planning abilities. Using the board game Settlers of Catan, accessed
through the open-source Catanatron framework, we benchmark a progression of
LLM-based agents, from a simple game-playing agent to systems capable of
autonomously rewriting their own prompts and their player agent's code. We
introduce a multi-agent architecture in which specialized roles (Analyzer,
Researcher, Coder, and Player) collaborate to iteratively analyze gameplay,
research new strategies, and modify the agent's logic or prompt. By comparing
manually crafted agents to those evolved entirely by LLMs, we evaluate how
effectively these systems can diagnose failure and adapt over time. Our results
show that self-evolving agents, particularly when powered by models like Claude
3.7 and GPT-4o, outperform static baselines by autonomously adopting their
strategies, passing along sample behavior to game-playing agents, and
demonstrating adaptive reasoning over multiple iterations.
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Nikolas Belle
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2025-06-11T08:36:30.021Z
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Nik Belle
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Dakota Barnes
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2025-06-11T08:36:32.151Z
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Dakota Barnes
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Dakotazoid56
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Alfonso Amayuelas
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2025-06-10T09:19:45.812Z
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Alfonso Amayuelas
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amayuelas
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Ivan Bercovich
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2025-06-10T02:17:28.751Z
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Ivan Bercovich
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ibercovich
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Xin Eric Wang
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2025-06-11T08:36:34.233Z
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Xin Eric Wang
user
xw-eric
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William Wang
Agents of Change: Self-Evolving LLM Agents for Strategic Planning
Recent advances in LLMs have enabled their use as autonomous agents across a
range of tasks, yet they continue to struggle with formulating and adhering to
coherent long-term strategies. In this paper, we investigate whether LLM agents
can self-improve when placed in environments that explicitly challenge their
strategic planning abilities. Using the board game Settlers of Catan, accessed
through the open-source Catanatron framework, we benchmark a progression of
LLM-based agents, from a simple game-playing agent to systems capable of
autonomously rewriting their own prompts and their player agent's code. We
introduce a multi-agent architecture in which specialized roles (Analyzer,
Researcher, Coder, and Player) collaborate to iteratively analyze gameplay,
research new strategies, and modify the agent's logic or prompt. By comparing
manually crafted agents to those evolved entirely by LLMs, we evaluate how
effectively these systems can diagnose failure and adapt over time. Our results
show that self-evolving agents, particularly when powered by models like Claude
3.7 and GPT-4o, outperform static baselines by autonomously adopting their
strategies, passing along sample behavior to game-playing agents, and
demonstrating adaptive reasoning over multiple iterations.
LLM agents improve their strategic planning and adapt over time when placed in complex environments like Settlers of Catan, outperforming static baselines through self-evolving mechanisms and collaboration among specialized roles.
LLMs autonomous agents strategic planning Settlers of Catan Catanatron game-playing agents self-improving agents multi-agent architecture Analyzer Researcher Coder Player adaptive reasoning Claude 3.7 GPT-4o