Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing field
This addresses reproducibility issues in DRL research for the AI community, though it is incremental as it builds on existing benchmarks and methods.
The paper tackles inconsistent evaluation in Deep Reinforcement Learning (DRL) on Atari by introducing SABER, a standardized benchmark, and finds that previous claims of superhuman DRL performance may be inaccurate, while also proposing Rainbow-IQN which achieves new state-of-the-art results.
Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL) is not straightforward. In the Arcade Learning Environment (ALE), small changes in environment parameters such as stochasticity or the maximum allowed play time can lead to very different performance. In this work, we discuss the difficulties of comparing different agents trained on ALE. In order to take a step further towards reproducible and comparable DRL, we introduce SABER, a Standardized Atari BEnchmark for general Reinforcement learning algorithms. Our methodology extends previous recommendations and contains a complete set of environment parameters as well as train and test procedures. We then use SABER to evaluate the current state of the art, Rainbow. Furthermore, we introduce a human world records baseline, and argue that previous claims of expert or superhuman performance of DRL might not be accurate. Finally, we propose Rainbow-IQN by extending Rainbow with Implicit Quantile Networks (IQN) leading to new state-of-the-art performance. Source code is available for reproducibility.