SafeDiSTL

New frameworks enhance safety in multi-agent and sampling-based reinforcement learning

Researchers have developed new frameworks for safe reinforcement learning (RL) to address challenges in real-world deployment. One approach, SafeDiSTL, decomposes joint constraints in multi-agent RL tasks, allowing for …