# rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks

arXiv: https://arxiv.org/abs/2102.04916 · HTML: https://mlsanity.com/paper/2102.04916/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [DeepMind Control Suite](https://mlsanity.com/paper/1801.00690/) (2018)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019)
- [robosuite: A Modular Simulation Framework and Benchmark for Robot Learning](https://mlsanity.com/paper/2009.12293/) (2020)
- [Leveraging Procedural Generation to Benchmark Reinforcement Learning](https://mlsanity.com/paper/1912.01588/) (2019)
- [dm_control: Software and Tasks for Continuous Control](https://mlsanity.com/paper/2006.12983/) (2020)
- [Behaviour Suite for Reinforcement Learning](https://mlsanity.com/paper/1908.03568/) (2019)
- [DeepMind Lab](https://mlsanity.com/paper/1612.03801/) (2016)
- [Gotta Learn Fast: A New Benchmark for Generalization in RL](https://mlsanity.com/paper/1804.03720/) (2018)
- [REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning](https://mlsanity.com/paper/1905.07447/) (2019)
- [robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots](https://mlsanity.com/paper/2007.02753/) (2020)
- [Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching Tasks](https://mlsanity.com/paper/2011.05782/) (2020)

## Built on by (most cited first, 3 of 3)

- [Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers](https://mlsanity.com/paper/2406.14325/) (2024)
- [Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environments](https://mlsanity.com/paper/2212.00906/) (2022)
- [Reliable validation of Reinforcement Learning Benchmarks](https://mlsanity.com/paper/2203.01075/) (2022)

## Abstract (arXiv)

Training reinforcement learning agents at solving a given task is highly dependent on identifying optimal sets of hyperparameters and selecting suitable environment input / output configurations. This tedious process could be eased with a straightforward toolbox allowing its user to quickly compare different training parameter sets. We present rl_reach, a self-contained, open-source and easy-to-use software package designed to run reproducible reinforcement learning experiments for customisable robotic reaching tasks. rl_reach packs together training environments, agents, hyperparameter optimisation tools and policy evaluation scripts, allowing its users to quickly investigate and identify optimal training configurations. rl_reach is publicly available at this URL: https://github.com/PierreExeter/rl_reach.