Safe Reinforcement Learning using Ideas from Model Predictive Control

This paper proposes a safe reinforcement learning framework that incorporates principles from Model Predictive Control (MPC) to ensure constraint satisfaction during agent training and deployment. The approach is particularly relevant for high-stakes applications where safety guarantees are critical, combining RL's adaptive learning with MPC's formal safety constraints.

This paper proposes a safe reinforcement learning framework that incorporates principles from Model Predictive Control (MPC) to ensure constraint satisfaction during agent training and deployment. The approach is particularly relevant for high-stakes applications where safety guarantees are critical, combining RL's adaptive learning with MPC's formal safety constraints.

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