MARGoS: A Configuration-Driven Platform for Reproducible MARL Experiments in ARGoS
[Inżynieria oprogramowania] Autorzy: Tom Philipp Backert, Mohammad D. Soorati, Javad Ghofrani
Multi-agent reinforcement learning (MARL) has become a prominent research direction, particularly within collective robotics, where the ARGoS simulator is widely used to study emergent swarm behavior. Yet despite this growth, systematic support for reuse and reproducibility in MARL-based swarm robotics remains limited and insufficiently standardized: a single experiment is assembled from manually aligned simulator files, training scripts, configuration values, paths, seeds, and environment details, so reuse and reproduction stay fragile even within one research group. To address this gap, we introduce MARGoS, a lightweight, configuration-driven platform developed in this project that elevates these experiment-defining inputs to first-class, platform-managed artifacts. MARGoS enables reproducible experimentation through configuration hashing, end-to-end seed propagation, frozen (immutabl…
- Kategoria arxiv
- cs.SE
- Kategoria
- Inżynieria oprogramowania
- Doi
- 10.1007/978-3-032-40421-3_24