Beyond Specialization: Robust Reinforcement Learning Navigation via Procedural Map Generators
Deep reinforcement learning (DRL) navigation policies often overfit to the structure of their training environments, as environmental diversity is typically constrained by the manual effort required to design diverse scenarios. While procedural map generation offers scalable diversity, no prior work systematically compares how different generator types affect policy generalization. We integrate four generators (sparse, maze, graph, and Wave Function Collapse) with guaranteed navigability into MuRoSim, a 2D simulator focusing on training efficiency for LiDAR-based navigation. We cross-evaluate five navigation policies on 1000 seeded maps per generator across three training seeds. Results s...
| Authors | Christian Jestel, Nicolas Bach, Marvin Wiedemann et al. |
|---|---|
| Primary category | cs.RO |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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