A hierarchical multi-agent RL method that selects skills at a high level and enforces pointwise safety with learned CBF-QP policies achieves about 99 percent success in simulated traffic scenarios.
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Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systems
A hierarchical multi-agent RL method that selects skills at a high level and enforces pointwise safety with learned CBF-QP policies achieves about 99 percent success in simulated traffic scenarios.