# A Comparison of Various Approaches to Reinforcement Learning Algorithms for Multi-robot Box Pushing

arXiv: https://arxiv.org/abs/1809.08337 · HTML: https://mlsanity.com/paper/1809.08337/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay](https://mlsanity.com/paper/2210.12892/) (2022)

## Abstract (arXiv)

In this paper, a comparison of reinforcement learning algorithms and their performance on a robot box pushing task is provided. The robot box pushing problem is structured as both a single-agent problem and also a multi-agent problem. A Q-learning algorithm is applied to the single-agent box pushing problem, and three different Q-learning algorithms are applied to the multi-agent box pushing problem. Both sets of algorithms are applied on a dynamic environment that is comprised of static objects, a static goal location, a dynamic box location, and dynamic agent positions. A simulation environment is developed to test the four algorithms, and their performance is compared through graphical explanations of test results. The comparison shows that the newly applied reinforcement algorithm out-performs the previously applied algorithms on the robot box pushing problem in a dynamic environment.