Code-Space Response Oracles: Generating Interpretable Multi-Agent Policies with Large Language Models
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 onl...
| Authors | Daniel Hennes, Zun Li, John Schultz et al. |
|---|---|
| Primary category | cs.GT |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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