2026
SDE-HARL: Scalable Distributed Policy Execution for Heterogeneous-Agent Reinforcement Learning
AAAI 2026technical
HARL enables agents to execute cooperative tasks by adopting agent-specific policies. Most of existing HARL methods use individual policy neural networks to ensure monotonic improvement, which leads to substantial computational overhead. The proposed SDE-HARL overcomes this limitation by decomposing