Deep learning is a rapidly growing field with dozens (editor: hah) of new publications each week on Arxiv. This group is a time set aside to go over interesting research from the previous week. We'll pick a one or a few papers to read and discuss.
UCSF Mission Hall Global Health & Clinical Sciences Building, Room 1406
This session we will discuss the research paper:
# Deep Reinforcement Learning that Matters
https://arxiv.org/abs/1709.06560 (make sure to select 2019 update)
Everyone should take time to read the paper in detail several days in advance of the meetup, and to the greatest extent possible, read the key references from the paper.
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, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.