TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning 事件

TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning arXiv:2602.01665v3 Announce Type: replace-cross Abstract: The design of environments plays a critical role in shaping the development and evaluation of cooperative multi-agent reinforcement learning (MARL) algorithms. While existing benchmarks highlight critical challenges, they often lack the modularity required to design custom evaluation scenarios. We introduce the Totally Accelerated Battle Simulator in