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 algorithms have shown tremendous success in simulation environments%2C but their application to real-world problems faces significant challenges%2C with safety being a major concern. In particular%2C enforcing state-wise constraints is essential for many challenging tasks such as autonomous driving and robot manipulation. However%2C existing safe RL algorithms under the framework of Constrained Markov Decision Process (CMDP) do not consider state-wise constraints. To address this gap%2C we propose State-wise Constrained Policy Optimization (SCPO)%2C the first general-purpose policy search algorithm for state-wise constrained reinforcement learning. SCPO provides guarantees for state-wise constraint satisfaction in expectation. In particular%2C we introduce the framework of Maximum Markov Decision Process%2C and prove that the worst-case safety violation is bounded under SCPO. We demonstrate the effectiveness of our approach on training neural network policies for extensive robot locomotion tasks%2C where the agent must satisfy a variety of state-wise safety constraints. Our results show that SCPO significantly outperforms existing methods and can handle state-wise constraints in high-dimensional robotics tasks.?quality=80&w=800)
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