Title | ||
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A Comparison of Various Approaches to Reinforcement Learning Algorithms for Multi-robot Box Pushing. |
Abstract | ||
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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. |
Year | Venue | Field |
---|---|---|
2018 | arXiv: Robotics | Computer science,Algorithm,Q-learning,Robot,Reinforcement,Reinforcement learning |
DocType | Volume | Citations |
Journal | abs/1809.08337 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Mehdi Rahimi | 1 | 1 | 1.42 |
Spencer Gibb | 2 | 3 | 2.12 |
Yantao Shen | 3 | 76 | 25.35 |
Hung Manh La | 4 | 344 | 36.19 |