Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning

Understand

Safety is essential for reinforcement learning (RL) applied in real-world tasks like autonomous driving.

• Chance constraints which guarantee the satisfaction of state constraints at a high probability are suitable to represent the requirements in real-world environment with uncertainty.
• Existing chance constrained RL methods like the penalty method and the Lagrangian method either exhibit periodic oscillations or cannot satisfy the constraints.
• In this paper, we address these shortcomings by proposing a separated proportional-integral Lagrangian (SPIL) algorithm.