StarCraft II micromanagement benchmark

The 'StarCraft II micromanagement benchmark' is a dataset used to evaluate the performance of multi-agent reinforcement learning algorithms in complex, real-time strategy scenarios involving unit control and decision-making.

Papers using StarCraft II micromanagement benchmark (10)

Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics2025 · 1 citesVDFD: Multi-Agent Value Decomposition Framework with Disentangled World Model2023RODE: Learning Roles to Decompose Multi-Agent Tasks2020 · 62 citesOff-Policy Multi-Agent Decomposed Policy Gradients2020 · 42 citesROMA: Multi-Agent Reinforcement Learning with Emergent Roles2020 · 32 citesRegularized Softmax Deep Multi-Agent $Q$-Learning2021 · 16 citesUneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning2020 · 7 citesSelf-Motivated Multi-Agent Exploration2023 · 2 citesContainerized Distributed Value-Based Multi-Agent Reinforcement Learning2021Multi-Agent Cooperation via Unsupervised Learning of Joint Intentions2023