Policy Iteration Achieves Regularized Equilibrium under Time Inconsistency
Yu-Jui Huang, Xiang Yu, Keyu Zhang

TL;DR
This paper introduces a policy iteration algorithm for entropy-regularized, time-inconsistent stochastic control problems, proving its exponential convergence to an equilibrium policy via a novel PDE approach.
Contribution
It develops a new policy iteration method for time-inconsistent control problems and proves its exponential convergence to equilibrium, addressing the existence and uniqueness of solutions.
Findings
Proves exponential convergence of the policy iteration algorithm.
Establishes the existence and uniqueness of classical solutions to the EEHJB equation.
Provides a constructive proof of equilibrium policy existence in complex stochastic control.
Abstract
For a general entropy-regularized time-inconsistent stochastic control problem, we propose a policy iteration algorithm (PIA) and establish its convergence to an equilibrium policy with an exponential convergence rate. The design of the PIA is based on a coupled system of non-local partial differential equations, called the exploratory equilibrium Hamilton--Jacobi--Bellman (EEHJB) equation. As opposed to the standard time-consistent case, policy improvement fails in general and the target value function (now an equilibrium value function) is not even known to exist a priori. To overcome these, we prove that the value functions generated by the PIA form a Cauchy sequence in a specialized Banach space, hence admit a limit, and the rate of convergence is exponential, on the strength of the Bismut--Elworthy--Li formula of stochastic representation. The limiting value function is shown to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdaptive Dynamic Programming Control · Optimization and Variational Analysis · Reinforcement Learning in Robotics
