mlsanityData to 7 Oct 2026

OpenRL: A Unified Reinforcement Learning Framework

arXiv 2312.16189 ↗First posted 20 Dec 2023cs.LG cs.AIcited by 1

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Abstract (arXiv)

We present OpenRL, an advanced reinforcement learning (RL) framework designed to accommodate a diverse array of tasks, from single-agent challenges to complex multi-agent systems. OpenRL's robust support for self-play training empowers agents to develop advanced strategies in competitive settings. Notably, OpenRL integrates Natural Language Processing (NLP) with RL, enabling researchers to address a combination of RL training and language-centric tasks effectively. Leveraging PyTorch's robust capabilities, OpenRL exemplifies modularity and a user-centric approach. It offers a universal interface that simplifies the user experience for beginners while maintaining the flexibility experts require for innovation and algorithm development. This equilibrium enhances the framework's practicality, adaptability, and scalability, establishing a new standard in RL research. To delve into OpenRL's features, we invite researchers and enthusiasts to explore our GitHub repository at https://github.com/OpenRL-Lab/openrl and access our comprehensive documentation at https://openrl-docs.readthedocs.io.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 35 references that are in the map. First posted on arXiv 20 Dec 2023.

Builds on

25 of 35 references.

References · 25

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  5. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  6. 2015Continuous control with deep reinforcement learningcited by 3,279
  7. 2020Learning to summarize from human feedbackcited by 2,562
  8. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  9. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,406
  10. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  11. 2018Exploration by Random Network Distillationcited by 966
  12. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
13 more
  1. 2018Diversity is All You Need: Learning Skills without a Reward Functioncited by 734
  2. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  3. 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 649
  4. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  5. 2022Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimizationcited by 244
  6. 2022Multi-Agent Reinforcement Learning is a Sequence Modeling Problemcited by 153
  7. 2023Extreme Parkour with Legged Robotscited by 120
  8. 2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 113
  9. 2021NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learningcited by 63
  10. 2020BeBold: Exploration Beyond the Boundary of Explored Regionscited by 32
  11. 2022PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learningcited by 18
  12. 2023TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Playcited by 18
  13. 2022DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimizationcited by 10

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Citing papers per year: 2025: 1.

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  1. 2025State Combinatorial Generalization In Decision Making With Conditional Diffusion Modelscited by 1