SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion PlanningJan 1, 2024·Jianye Xu,Pan Hu,Bassam Alrifaee· 0 min read Cite DOITypeConference paperPublication2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)Last updated on Jan 1, 2024 ← Limiting Computation Levels in Prioritized Trajectory Planning with Safety Guarantees Jan 1, 2024XP-MARL: Auxiliary Prioritization in Multi-Agent Reinforcement Learning to Address Non-Stationarity Jan 1, 2024 →