Recent advances in multi-agent reinforcement learning, particularly Policy-Space Response Oracles (PSRO), have enabled the computation of approximate game-theoretic equilibria in increasingly complex domains. However, these methods rely on deep reinforcement learning oracles that produce `black-box' neural network policies, making them difficult to interpret, trust or debug. We introduce Code-Space Response Oracles (CSRO), a novel framework that addresses this challenge by replacing RL oracles with Large Language Models (LLMs). CSRO reframes the best response computation as a code generation task, prompting an LLM to generate policies directly as human-readable code. This approach not only yields inherently interpretable policies but also leverages the LLM's pretrained knowledge to discover complex, human-like strategies. We explore multiple ways to construct and enhance an LLM-based oracle: zero-shot prompting, iterative refinement and \emph{AlphaEvolve}, a distributed LLM-based evolutionary system. We demonstrate that CSRO achieves performance competitive with baselines while producing a diverse set of explainable policies. Our work presents a new perspective on multi-agent learning, shifting the focus from optimizing opaque policy parameters to synthesizing interpretable algorithmic behavior.

翻译：近年来，多智能体强化学习领域，特别是策略空间响应预言机（PSRO）的发展，使得在日益复杂的领域中计算近似博弈论均衡成为可能。然而，这些方法依赖于产生“黑盒”神经网络策略的深度强化学习预言机，导致策略难以解释、信任或调试。我们提出了代码空间响应预言机（CSRO），这一新颖框架通过用大型语言模型（LLM）替代强化学习预言机来解决这一挑战。CSRO将最佳响应计算重新定义为代码生成任务，提示LLM直接生成人类可读的代码作为策略。这种方法不仅产生本质上可解释的策略，还利用LLM的预训练知识来发现复杂的、类人策略。我们探索了多种构建和增强基于LLM的预言机的方法：零样本提示、迭代优化以及\textit{AlphaEvolve}——一个基于LLM的分布式进化系统。我们证明，CSRO在保持与基线方法相当性能的同时，能产生一组多样化的可解释策略。我们的工作为多智能体学习提供了新的视角，将重点从优化不透明的策略参数转向合成可解释的算法行为。