Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
METADATA ONLY
Loading...
Author / Producer
Date
2024-02-24
Publication Type
Working Paper
ETH Bibliography
no
Citations
Altmetric
METADATA ONLY
Data
Rights / License
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.
Permanent link
Publication status
published
Editor
Book title
Journal / series
Volume
Pages / Article No.
2402.03046
Publisher
Cornell University
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Computer Science - Machine Learning
Organisational unit
09828 - Fuge, Mark / Fuge, Mark