Reinforcement learning for irreversible reinsurance problems: the randomized singular control approach

This paper studies the continuous-time reinforcement learning for stochastic singular control with the application to an infinite-horizon irreversible reinsurance problem.

The singular control is equivalently characterized as a pair of regions of time and the augmented states, called the singular control law.

To encourage the exploration in the learning procedure, we propose a randomization method by considering an auxiliary singular control and entropy regularization.

The exploratory singular control problem is formulated as a two-stage optimal control problem, in which the time-inconsistency issue arises in the outer problem.

Existence of equilibrium singular control law for the time-inconsistent outer problem is rigorously established.

Taking advantage of the solution structure, we utilize a proper parameterization and neural networks to devise the actor-critic reinforcement learning algorithm.

In the numerical experiment, we show the superior convergence of parameter iterations based on the randomized equilibrium policy and illustrate how the exploration may advance the learning performance.