DiffOP: Reinforcement Learning of Optimization-Based Control Policies via Implicit Policy Gradients

Understand

Real-world control systems require policies that are not only high-performing but also interpretable and robust.

• A promising direction toward this goal is model-based control, which learns system dynamics and cost functions from historical data and then uses these models to inform decision-making.
• Building on this paradigm, we introduce DiffOP, a novel framework for learning optimization-based control policies defined implicitly through optimization control problems.
• Without relying on value function approximation, DiffOP jointly learns the cost and dynamics models and directly optimizes the actual control costs using policy gradients.