A Scalable Finite Difference Method for Deep Reinforcement Learning

TL;DR

This paper introduces a scalable finite difference method for deep reinforcement learning that efficiently utilizes older data, reducing idle time and computational waste in distributed settings.

Contribution

A novel finite difference algorithm that leverages older data to improve scalability and efficiency in distributed deep reinforcement learning.

Findings

Reduces idle time in distributed algorithms

Maintains performance comparable to existing methods

Improves computational efficiency in large-scale setups

Abstract

Several low-bandwidth distributable black-box optimization algorithms in the family of finite differences such as Evolution Strategies have recently been shown to perform nearly as well as tailored Reinforcement Learning methods in some Reinforcement Learning domains. One shortcoming of these black-box methods is that they must collect information about the structure of the return function at every update, and can often employ only information drawn from a distribution centered around the current parameters. As a result, when these algorithms are distributed across many machines, a significant portion of total runtime may be spent with many machines idle, waiting for a final return and then for an update to be calculated. In this work we introduce a novel method to use older data in finite difference algorithms, which produces a scalable algorithm that avoids significant idle time or wasted computation.