State-wise Constrained Policy Optimization

Understand

Reinforcement Learning (RL) algorithms have shown tremendous success in simulation environments, but their application to real-world problems faces significant challenges, with safety being a major concern.

• In particular, enforcing state-wise constraints is essential for many challenging tasks such as autonomous driving and robot manipulation.
• However, existing safe RL algorithms under the framework of Constrained Markov Decision Process (CMDP) do not consider state-wise constraints.
• To address this gap, we propose State-wise Constrained Policy Optimization (SCPO), the first general-purpose policy search algorithm for state-wise constrained reinforcement learning.