Researchers have introduced RideGym, an open-source, standardized interface designed for multi-agent reinforcement learning (MARL) in real-world ride-sharing systems. This Gym-style environment aims to address the lack of reproducibility and fair comparison in existing simulation platforms by decoupling the environment from dispatch algorithms. RideGym supports large-scale city simulations on real road networks with flexible configurations and has demonstrated high efficiency, completing hour-long simulations in under a minute. The platform also highlights the significant impact of exploration noise on MARL solution performance and ranking. AI
IMPACT Standardizes MARL research for ride-sharing, enabling better comparison and faster development of dispatch algorithms.
RANK_REASON The item describes a new open-source interface for multi-agent reinforcement learning in ride-sharing systems, presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.MA (Multiagent) →
- alphaXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Gym-style
- Hugging Face
- Influence Flower
- Multi-agent reinforcement learning
- RideGym
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