Deep Reinforcement Learning that Matters

Understand

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.