Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning

• URL: http://arxiv.org/abs/2402.03046v1
• Date: Mon, 5 Feb 2024 14:32:00 GMT
• Title: Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
• Authors: Shengyi Huang and Quentin Gallou\'edec and Florian Felten and Antonin Raffin and Rousslan Fernand Julien Dossa and Yanxiao Zhao and Ryan Sullivan and Viktor Makoviychuk and Denys Makoviichuk and Mohamad H. Danesh and Cyril Roum\'egous and Jiayi Weng and Chufan Chen and Md Masudur Rahman and Jo\~ao G. M. Ara\'ujo and Guorui Quan and Daniel Tan and Timo Klein and Rujikorn Charakorn and Mark Towers and Yann Berthelot and Kinal Mehta and Dipam Chakraborty and Arjun KG and Valentin Charraut and Chang Ye and Zichen Liu and Lucas N. Alegre and Alexander Nikulin and Xiao Hu and Tianlin Liu and Jongwook Choi and Brent Yi
• Abstract summary: We present Open RL Benchmark, a set of fully tracked RL experiments. Open RL Benchmark is community-driven: anyone can download, use, and contribute to the data. Special care is taken to ensure that each experiment is precisely reproducible.
• Score: 41.971465819626005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores [13.948640763797776]
  We present a novel abstraction on the dataflows of RL training, which unifies diverse RL training applications into a general framework. We develop a scalable, efficient, and distributed RL system called ReaLly scalableRL, which allows efficient and massively parallelized training. SRL is the first in the academic community to perform RL experiments at a large scale with over 15k CPU cores.
  arXiv  Detail & Related papers  (2023-06-29T05:16:25Z)
• RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$ [12.111848705677142]
  We propose RL$3$, a hybrid approach that incorporates action-values, learned per task through traditional RL, in the inputs to meta-RL. We show that RL$3$ earns greater cumulative reward in the long term, compared to RL$2$, while maintaining data-efficiency in the short term, and generalizes better to out-of-distribution tasks.
  arXiv  Detail & Related papers  (2023-06-28T04:16:16Z)
• Improving and Benchmarking Offline Reinforcement Learning Algorithms [87.67996706673674]
  This work aims to bridge the gaps caused by low-level choices and datasets. We empirically investigate 20 implementation choices using three representative algorithms. We find two variants CRR+ and CQL+ achieving new state-of-the-art on D4RL.
  arXiv  Detail & Related papers  (2023-06-01T17:58:46Z)
• LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement Learning [78.2286146954051]
  LCRL implements model-free Reinforcement Learning (RL) algorithms over unknown Decision Processes (MDPs) We present case studies to demonstrate the applicability, ease of use, scalability, and performance of LCRL.
  arXiv  Detail & Related papers  (2022-09-21T13:21:00Z)
• ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives [11.675763847424786]
  We present ShinRL, an open-source library for evaluation of reinforcement learning (RL) algorithms. ShinRL provides an RL environment interface that can compute metrics for delving into the behaviors of RL algorithms. We show how combining these two features of ShinRL makes it easier to analyze the behavior of deep Q learning.
  arXiv  Detail & Related papers  (2021-12-08T05:34:46Z)
• RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads [4.575381867242508]
  We propose RL-Scope, a cross-stack profiler that scopes low-level CPU/GPU resource usage to high-level algorithmic operations. We demonstrate RL-Scope's utility through in-depth case studies.
  arXiv  Detail & Related papers  (2021-02-08T15:42:48Z)
• Learning to Prune Deep Neural Networks via Reinforcement Learning [64.85939668308966]
  PuRL is a deep reinforcement learning based algorithm for pruning neural networks. It achieves sparsity and accuracy comparable to current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-09T13:06:07Z)
• RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning [108.9599280270704]
  We propose a benchmark called RL Unplugged to evaluate and compare offline RL methods. RL Unplugged includes data from a diverse range of domains including games and simulated motor control problems. We will release data for all our tasks and open-source all algorithms presented in this paper.
  arXiv  Detail & Related papers  (2020-06-24T17:14:51Z)
• MushroomRL: Simplifying Reinforcement Learning Research [60.70556446270147]
  MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a comprehensive and flexible framework to minimize the effort in implementing and testing novel RL methodologies.
  arXiv  Detail & Related papers  (2020-01-04T17:23:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.