HypRL: Reinforcement Learning of Control Policies for Hyperproperties

Reward shaping in multi-agent reinforcement learning (MARL) for complex tasks remains a significant challenge. Existing approaches often fail to find optimal solutions or cannot efficiently handle such tasks. We propose HypRL, a specification-guided reinforcement learning framework that learns control policies w.r.t.