Agents of Change: Self-Evolving LLM Agents for Strategic Planning (2506.04651v1)

Abstract: 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.

Analysis of Self-Evolving LLM Agents for Strategic Planning

The paper "Agents of Change: Self-Evolving LLM Agents for Strategic Planning" provides a comprehensive paper on the autonomous evolution of LLM agents in environments demanding strategic planning, using Settlers of Catan as a complex testbed. This work explores whether LLM agents, faced with environments that explicitly challenge their strategic competencies, can improve their capabilities without direct human intervention.

The authors employ an innovative framework using the Catanatron environment to test varying architectures of LLM agents, ranging from basic forms to sophisticated systems capable of self-generating strategies and rewriting their operational code. The experimental design includes four key architectures: BaseAgent, StructuredAgent, PromptEvolver, and AgentEvolver, progressively enhancing autonomy and strategic depth. These agents are compared against a static heuristic-based AlphaBeta bot to evaluate strategic reasoning and long-term planning capacities.

The results indicate that self-evolving agents, particularly those using Claude 3.7 and GPT-4o, notably surpass static baselines. Claude 3.7 demonstrated remarkable improvements in strategic depth, primarily via refined prompt adjustments conducive to coherent long-term planning. The PromptEvolver agent framework achieved significant enhancements in strategy development over successive iterations, showing a marked improvement over baseline performance in achieving coherent strategic objectives.

However, while demonstrating the potential of autonomous LLM-driven strategies, the paper also reveals inherent limitations, particularly regarding computational overhead and scalability. The effectiveness of the evolution process is heavily contingent on the underlying model's capabilities, showcasing variability in strategic advancements across different LLM architectures. Additionally, the randomness and partial observability in Settlers of Catan impose challenges for precise strategic adaptation by LLMs.

The implications of this research extend beyond the confines of board games, offering insights into the future capabilities of LLMs as autonomous designers and planners in varied fields. Strategies derived from competitive environments like Settlers of Catan could inform developments in cooperative AI and negotiations in multi-agent scenarios. Looking ahead, further exploration into integrating symbolic reasoning with LLM-based architectures could yield more robust self-improving agents, optimizing strategic interactions across diverse contexts.

The findings suggest a promising direction for AI development, showcasing the potential for LLM-based systems to evolve autonomously and heralding new possibilities in complex strategic planning domains. This paper not only underscores the capabilities of LLMs to be more than passive participants but also themes toward active, strategic innovativeness in artificial agents.