Deep Reinforcement Learning at the Edge of the Statistical Precipice with Rishabh Agarwal - #559
Published:Feb 14, 2022 17:57
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode discussing a research paper on Deep Reinforcement Learning (DRL). The paper, which won an award at NeurIPS, critiques the common practice of evaluating DRL algorithms using only point estimates on benchmarks with a limited number of runs. The researchers, including Rishabh Agarwal, found significant discrepancies between conclusions drawn from point estimates and those from statistical analysis, particularly when using benchmarks like Atari 100k. The podcast explores the paper's reception, surprising results, and the challenges of changing self-reporting practices in research.
Key Takeaways
- •The paper highlights the potential for misleading conclusions when evaluating DRL algorithms with limited runs and relying solely on point estimates.
- •Statistical analysis is crucial for accurately assessing the performance of DRL algorithms, especially on benchmarks.
- •The research raises questions about the incentives and challenges associated with changing reporting practices in the research community.
Reference
“The paper calls for a change in how deep RL performance is reported on benchmarks when using only a few runs.”