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Implementation of Q learning and deep Q network for controlling a self balancing robot model
In this paper, the implementations of two reinforcement learnings namely, Q learning and deep Q network (DQN) on the Gazebo model of a self balancing robot have been discussed. The goal of the experiments is to make the robot model learn the best actions for staying balanced in an environment. The m...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer Berlin Heidelberg
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302870/ https://www.ncbi.nlm.nih.gov/pubmed/30613463 http://dx.doi.org/10.1186/s40638-018-0091-9 |