Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning (2402.03046v1)

Abstract: In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone. We present Open RL Benchmark, a set of fully tracked RL experiments, including not only the usual data such as episodic return, but also all algorithm-specific and system metrics. Open RL Benchmark is community-driven: anyone can download, use, and contribute to the data. At the time of writing, more than 25,000 runs have been tracked, for a cumulative duration of more than 8 years. Open RL Benchmark covers a wide range of RL libraries and reference implementations. Special care is taken to ensure that each experiment is precisely reproducible by providing not only the full parameters, but also the versions of the dependencies used to generate it. In addition, Open RL Benchmark comes with a command-line interface (CLI) for easy fetching and generating figures to present the results. In this document, we include two case studies to demonstrate the usefulness of Open RL Benchmark in practice. To the best of our knowledge, Open RL Benchmark is the first RL benchmark of its kind, and the authors hope that it will improve and facilitate the work of researchers in the field.

• The paper introduces a benchmark that standardizes reproducible RL experiments across various libraries and environments.
• The paper details a command-line interface (CLI) that simplifies data extraction, visualization, and rigorous comparisons among RL methods.
• The paper enhances research transparency by providing complete replication instructions, including hyperparameters and dependency details.

Introduction to Open RL Benchmark

In the pursuit of advancing Reinforcement Learning (RL), researchers require reliable benchmarks to evaluate new algorithmic approaches against established baselines. The lack of comprehensive, accessible, and reproducibly tracked experiments has been a substantial barrier in the field. Open RL Benchmark emerges as a solution, offering an extensive dataset of tracked RL experiments that encompasses various libraries, environments, and metrics. By providing fully documented and replicable experiment settings, this benchmark facilitates the comparison of RL methods and supports the efficient exploration of new ideas in RL research.

Insights into Reproducibility and Data Accessibility

Reproducibility lies at the core of scientific progress. In RL, the lack of detailed documentation, evolving software dependencies, and the idiosyncrasies of implementation can substantially influence experimental results and their reproducibility. Open RL Benchmark addresses these challenges head-on by offering exact experiment replication instructions, including all hyperparameters and dependencies. This initiative furthers the research community's ability to engage with intricate learning phenomena and exceptional events that might otherwise be neglected in result summaries.

The Open RL Benchmark CLI and Its Applications

A remarkable component of Open RL Benchmark is its powerful command-line interface (CLI), designed to ease the extraction, analysis, and visualization of data. The CLI is a one-stop shop that allows researchers to produce data visualizations and figures tailored for research publication with simplicity and precision. Notably, every figure included in the benchmark's related document has been generated through this CLI, demonstrating its utility.

Transformative Impact of Open RL Benchmark on RL Research

The introduction of the Open RL Benchmark has laid the groundwork for more standardized, transparent, and accessible RL research. It enables researchers to build upon existing datasets rather than expending resources on baseline reproductions, offering an unprecedented level of detail and clarity. However, as the benchmark grows with community contributions, the challenge remains to maintain user friendliness and manage the scale of engagement.

Conclusion

Open RL Benchmark stands as a significant stride toward resolving long-standing reproducibility and evaluation challenges in RL research. It democratizes the access to rich datasets, ensuring reliable comparisons and fostering a more profound comprehension of algorithms' performance dynamics. Despite potential difficulties in scaling and maintaining the efficiency of user collaboration, Open RL Benchmark's contributions offer a leap forward in setting higher standards for RL research.