Policy-based optimization: single-step policy gradient method seen as an evolution strategy

TL;DR

This paper introduces Policy-Based Optimization (PBO), a black-box optimization method inspired by policy gradient and evolution strategies, demonstrating its effectiveness on standard functions and control law optimization.

Contribution

It presents the PBO algorithm as a novel, versatile black-box optimization technique with detailed description and initial validation, bridging policy gradient and evolution strategies.

Findings

PBO performs well on standard analytic functions.

PBO is effective for optimizing control laws for the Lorenz attractor.

The method shows promise as a flexible optimization tool.

Abstract

This research reports on the recent development of a black-box optimization method based on single-step deep reinforcement learning (DRL), and on its conceptual proximity to evolution strategy (ES) techniques. In the fashion of policy gradient (PG) methods, the policy-based optimization (PBO) algorithm relies on the update of a policy network to describe the density function of its next generation of individuals. The method is described in details, and its similarities to both ES and PG methods are pointed out. The relevance of the approach is then evaluated on the minimization of standard analytic functions, with comparison to classic ES techniques (ES, CMA-ES). It is then applied to the optimization of parametric control laws designed for the Lorenz attractor. Given the scarce existing literature on the method, this contribution definitely establishes the PBO method as a valid, versatile black-box optimization technique, and opens the way to multiple future improvements allowed by the flexibility of the neural networks approach.