Distributed Asynchronous Policy Iteration for Sequential Zero-Sum Games and Minimax Control

TL;DR

This paper presents a new, convergent, and parallelizable policy iteration framework for sequential zero-sum games and minimax problems, overcoming longstanding convergence issues of natural algorithms.

Contribution

It introduces a contractive dynamic programming framework with algorithms that ensure convergence and enable distributed asynchronous implementation for complex minimax problems.

Findings

Algorithms resolve convergence difficulties of natural policy iteration.

Framework supports parallel and distributed implementation.

Suitable for large-scale reinforcement learning applications.

Abstract

We introduce a contractive abstract dynamic programming framework and related policy iteration algorithms, specifically designed for sequential zero-sum games and minimax problems with a general structure. Aside from greater generality, the advantage of our algorithms over alternatives is that they resolve some long-standing convergence difficulties of the "natural" policy iteration algorithm, which have been known since the Pollatschek and Avi-Itzhak method [PoA69] for finite-state Markov games. Mathematically, this "natural" algorithm is a form of Newton's method for solving Bellman's equation, but Newton's method, contrary to the case of single-player DP problems, is not globally convergent in the case of a minimax problem, because the Bellman operator may have components that are neither convex nor concave. Our algorithms address this difficulty by introducing alternating player choices, and by using a policy-dependent mapping with a uniform sup-norm contraction property, similar to earlier works by Bertsekas and Yu [BeY10], [BeY12], [YuB13]. Moreover, our algorithms allow a convergent and highly parallelizable implementation, which is based on state space partitioning, and distributed asynchronous policy evaluation and policy improvement operations within each set of the partition. Our framework is also suitable for the use of reinforcement learning methods based on aggregation, which may be useful for large-scale problem instances.