mlsanityData to 7 Oct 2026

JORLDY: a fully customizable open source framework for reinforcement learning

arXiv 2204.04892 ↗First posted 11 Apr 2022cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Recently, Reinforcement Learning (RL) has been actively researched in both academic and industrial fields. However, there exist only a few RL frameworks which are developed for researchers or students who want to study RL. In response, we propose an open-source RL framework "Join Our Reinforcement Learning framework for Developing Yours" (JORLDY). JORLDY provides more than 20 widely used RL algorithms which are implemented with Pytorch. Also, JORLDY supports multiple RL environments which include OpenAI gym, Unity ML-Agents, Mujoco, Super Mario Bros and Procgen. Moreover, the algorithmic components such as agent, network, environment can be freely customized, so that the users can easily modify and append algorithmic components. We expect that JORLDY will support various RL research and contribute further advance the field of RL. The source code of JORLDY is provided on the following Github: https://github.com/kakaoenterprise/JORLDY

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 42 references that are in the map. First posted on arXiv 11 Apr 2022.

Builds on

31 of 42 references.

References · 31

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  4. 2015Continuous control with deep reinforcement learningcited by 3,279
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  6. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  7. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,173
  8. 2015Prioritized Experience Replaycited by 1,070
  9. 2018Exploration by Random Network Distillationcited by 966
  10. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 774
  11. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  12. 2017A Distributional Perspective on Reinforcement Learningcited by 743
19 more
  1. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 516
  2. 2019Leveraging Procedural Generation to Benchmark Reinforcement Learningcited by 478
  3. 2017Distributional Reinforcement Learning with Quantile Regressioncited by 464
  4. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 362
  5. 2018Maximum a Posteriori Policy Optimisationcited by 318
  6. 2020Agent57: Outperforming the Atari Human Benchmarkcited by 300
  7. 2017Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agentscited by 267
  8. 2017Noisy Networks for Explorationcited by 262
  9. 2018Distributed Prioritized Experience Replaycited by 260
  10. 2018Visual Reinforcement Learning with Imagined Goalscited by 248
  11. 2018Unity: A General Platform for Intelligent Agentscited by 232
  12. 2021Reinforcement learning based recommender systems: A surveycited by 187
  13. 2015Deep Spatial Autoencoders for Visuomotor Learningcited by 149
  14. 2020Acme: A Research Framework for Distributed Reinforcement Learningcited by 146
  15. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 139
  16. 2019V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Controlcited by 85
  17. 2020Munchausen Reinforcement Learningcited by 68
  18. 2017RLlib: Abstractions for Distributed Reinforcement Learningcited by 53
  19. 2019ChainerRL: A Deep Reinforcement Learning Librarycited by 51

Built on by

Not cited yet.