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arXiv · 1809.08337

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

Mehdi Rahimi·Spencer Gibb·Yantao Shen·Hung Manh La
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Abstract

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.

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Mehdi Rahimi, Spencer Gibb, Yantao Shen, Hung Manh La. 2018-09-21. A Comparison of Various Approaches to Reinforcement Learning Algorithms for Multi-robot Box Pushing. https://arxiv.org/abs/1809.08337

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