# Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching Tasks

arXiv: https://arxiv.org/abs/2011.05782 · HTML: https://mlsanity.com/paper/2011.05782/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [RMP2: A Structured Composable Policy Class for Robot Learning](https://mlsanity.com/paper/2103.05922/) (2021)
- [RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator Control](https://mlsanity.com/paper/2210.11262/) (2022)
- [rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks](https://mlsanity.com/paper/2102.04916/) (2021)
- [Off-Policy Deep Reinforcement Learning Algorithms for Handling Various Robotic Manipulator Tasks](https://mlsanity.com/paper/2212.05572/) (2022)
- [Dual-Arm Adversarial Robot Learning](https://mlsanity.com/paper/2110.08066/) (2021)

## Abstract (arXiv)

Reinforcement learning has shown great promise in robotics thanks to its ability to develop efficient robotic control procedures through self-training. In particular, reinforcement learning has been successfully applied to solving the reaching task with robotic arms. In this paper, we define a robust, reproducible and systematic experimental procedure to compare the performance of various model-free algorithms at solving this task. The policies are trained in simulation and are then transferred to a physical robotic manipulator. It is shown that augmenting the reward signal with the Hindsight Experience Replay exploration technique increases the average return of off-policy agents between 7 and 9 folds when the target position is initialised randomly at the beginning of each episode.