Policy Search in Code Space for Multi-Agent Pathfinding
Developing coordination policies for complex multi-agent systems relies on either labor-intensive algorithm design or opaque task-specific reinforcement learning. With recent advances in autonomous coding agents, we show that policy search directly in code space offers a compelling third path that combines automated optimization with explicit interpretability. We investigate a human-directed workflow in which coding agents iteratively propose, implement, evaluate, diagnose, and eventually refine algorithms guided by simulation feedback and explicit correctness verification. We conducted two case studies and found that this workflow robustly outperformed any manually engineered solutions. ...
| Authors | Matthias Eder, Pantelis Emmanouil, Vivek Farias et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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