Deep Reinforcement Learning at the Edge of the Statistical Precipice (2108.13264v4)

Abstract: Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing their relative performance on a large suite of tasks. Most published results on deep RL benchmarks compare point estimates of aggregate performance such as mean and median scores across tasks, ignoring the statistical uncertainty implied by the use of a finite number of training runs. Beginning with the Arcade Learning Environment (ALE), the shift towards computationally-demanding benchmarks has led to the practice of evaluating only a small number of runs per task, exacerbating the statistical uncertainty in point estimates. In this paper, we argue that reliable evaluation in the few run deep RL regime cannot ignore the uncertainty in results without running the risk of slowing down progress in the field. We illustrate this point using a case study on the Atari 100k benchmark, where we find substantial discrepancies between conclusions drawn from point estimates alone versus a more thorough statistical analysis. With the aim of increasing the field's confidence in reported results with a handful of runs, we advocate for reporting interval estimates of aggregate performance and propose performance profiles to account for the variability in results, as well as present more robust and efficient aggregate metrics, such as interquartile mean scores, to achieve small uncertainty in results. Using such statistical tools, we scrutinize performance evaluations of existing algorithms on other widely used RL benchmarks including the ALE, Procgen, and the DeepMind Control Suite, again revealing discrepancies in prior comparisons. Our findings call for a change in how we evaluate performance in deep RL, for which we present a more rigorous evaluation methodology, accompanied with an open-source library rliable, to prevent unreliable results from stagnating the field.

• The paper identifies statistical flaws in conventional deep RL evaluations, showing that reliance on limited runs and point estimates often misleads performance conclusions.
• It proposes a robust evaluation framework using stratified bootstrap confidence intervals, performance profiles, and interquartile mean (IQM) to better capture uncertainty.
• The findings emphasize the need for rigorous statistical methods to improve reproducibility and genuine progress in deep reinforcement learning research.

Deep Reinforcement Learning at the Edge of the Statistical Precipice: An Analysis

In the paper titled "Deep Reinforcement Learning at the Edge of the Statistical Precipice," the authors highlight critical statistical issues in the evaluation of deep reinforcement learning (RL) algorithms. They argue that current methodologies, which often rely on insufficient runs and point estimates, lead to unreliable results, fostering misconceptions about progress in the field.

Key Contributions

The authors critique the prevalent use of mean and median scores, which fail to account for the statistical uncertainties inherent in RL evaluations. They demonstrate through a case paper on the Atari 100k benchmark that conclusions drawn from limited runs can significantly diverge from those derived using rigorous statistical analyses. Specifically, they identify large discrepancies between outcomes based solely on point estimates versus those incorporating uncertainty through confidence intervals (CIs).

Statistical Framework

The paper advocates for a more nuanced statistical approach to performance evaluation in deep RL. The authors propose three main tools:

1. Stratified Bootstrap Confidence Intervals: By re-sampling runs independently across tasks, this method provides a robust estimation of uncertainty, even in the few-run regime.
2. Performance Profiles: These profiles, particularly score distributions, offer a comprehensive view of an algorithm's performance variability across tasks and runs. Compared to traditional tables of mean scores, performance profiles are more resilient to outliers and capable of illustrating a distribution's shape.
3. Robust Aggregate Metrics: The paper suggests the use of Interquartile Mean (IQM) as a more statistically efficient and robust alternative to the mean and median. IQM is less sensitive to outliers while maintaining a smaller confidence interval in the few-run regime.

Case Studies and Findings

The authors provide a rigorous analysis of popular deep RL benchmarks, including the Arcade Learning Environment, Procgen, and the DeepMind Control Suite. Through these case studies, they reveal inconsistencies in previous reports, attributing them to inadequate statistical treatment and changes in evaluation protocols. Their analyses demonstrate significant overlaps in CIs for many algorithms considered state-of-the-art, pointing out that previously claimed improvements may be overestimated.

Implications and Future Directions

This work has profound implications for both the practical application and theoretical understanding of deep RL algorithms. By highlighting the pervasive statistical issues, the authors call for a shift towards more rigorous evaluation methodologies. This shift is anticipated to lead to more reliable comparisons, ultimately fostering genuine progress in deep RL by focusing on robust and reproducible findings.

The authors also present an open-source library, rliable, to facilitate the adoption of their recommended evaluation methodology, aiming to make these robust analytical tools accessible to researchers and practitioners in the field.

Conclusion

In summary, the paper emphasizes the necessity of addressing statistical uncertainties in deep RL evaluations. By adopting interval estimates, performance profiles, and robust aggregate metrics, the field can enhance the reliability of its reported results, thereby avoiding potential misdirections in research focus and application. This work represents a crucial step towards establishing sound experimental protocols in the domain of deep reinforcement learning.