mlsanityData to 7 Oct 2026

RL-X: A Deep Reinforcement Learning Library (not only) for RoboCup

arXiv 2310.13396 ↗First posted 20 Oct 2023cs.RO cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

This paper presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5x speedups compared to well-known frameworks like Stable-Baselines3.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 13 of its 28 references that are in the map. First posted on arXiv 20 Oct 2023.

Builds on

13 of 28 references.

References · 13

  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. 2018DeepMind Control Suitecited by 935
  5. 2021Randomized Ensembled Double Q-Learning: Learning Fast Without a Modelcited by 278
  6. 2020Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Criticscited by 154
  7. 2020Acme: A Research Framework for Distributed Reinforcement Learningcited by 146
  8. 2018Dopamine: A Research Framework for Deep Reinforcement Learningcited by 121
  9. 2021Dropout Q-Functions for Doubly Efficient Reinforcement Learningcited by 108
  10. 2022EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Enginecited by 58
  11. 2021Muesli: Combining Improvements in Policy Optimizationcited by 53
  12. 2022You May Not Need Ratio Clipping in PPOcited by 19
1 more
  1. 2021Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performancecited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 3.

7 papers.

  1. 2024One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotioncited by 34
  2. 2025Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotioncited by 7
  3. 2025Multi-Embodiment Locomotion at Scale with extreme Embodiment Randomizationcited by 2
  4. 2025Massively Scaling Explicit Policy-conditioned Value Functionscited by 1
  5. 2026Shape Your Body: Value Gradients for Multi-Embodiment Robot Designcited by 1
  6. 2026Trust Region Inverse Reinforcement Learning: Explicit Dual Ascent using Local Policy Updatescited by 0
  7. 2026ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learningcited by 0