Review paper
Deep Reinforcement Learning That Matters
Volume: 32, Issue: 1
Published: Apr 29, 2018
Abstract
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments,...
Paper Details
Title
Deep Reinforcement Learning That Matters
Published Date
Apr 29, 2018
Volume
32
Issue
1