Duality between density function and value function with applications in constrained optimal control and Markov Decision Process
For practitioners in robotics and traffic control, this provides a principled way to incorporate safety constraints into optimal control without ad-hoc modifications.
The paper establishes a duality between density and value functions in optimal control, enabling safety constraints to be enforced via a primal-dual algorithm. The method is validated on robot navigation, traffic control, and segway control problems.
Density function describes the density of states in the state space of a dynamic system or a Markov Decision Process (MDP). Its evolution follows the Liouville equation. We show that the density function is the dual of the value function in the optimal control problems. By utilizing the duality, constraints that are hard to enforce in the primal value function optimization such as safety constraints in robot navigation, traffic capacity constraints in traffic flow control can be posed on the density function, and the constrained optimal control problem can be solved with a primal-dual algorithm that alternates between the primal and dual optimization. The primal optimization follows the standard optimal control algorithm with a perturbation term generated by the density constraint, and the dual problem solves the Liouville equation to get the density function under a fixed control strategy and updates the perturbation term. Moreover, the proposed method can be extended to the case with exogenous disturbance, and guarantee robust safety under the worst-case disturbance. We apply the proposed method to three examples, a robot navigation problem and a traffic control problem in sim, and a segway control problem with experiment.