Solving the optimal path planning of a mobile robot using improved Q-learning
Q-learning, a type of reinforcement learning, has gained increasing popularity in autonomous mobile robot path planning recently, due to its self-learning ability without requiring a priori model of the environment. Yet, despite such advantage, Q-learning exhibits slow convergence to the optimal sol...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
Elsevier
2019
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4217/1/AJ%202019%20%28253%29.pdf http://eprints.uthm.edu.my/4217/ https://doi.org/10.1016/j.robot.2019.02.013 |
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| Summary: | Q-learning, a type of reinforcement learning, has gained increasing popularity in autonomous mobile robot path planning recently, due to its self-learning ability without requiring a priori model of the environment. Yet, despite such advantage, Q-learning exhibits slow convergence to the optimal solution. In order to address this limitation, the concept of partially guided Q-learning is introduced wherein, the flower pollination algorithm (FPA) is utilized to improve the initialization of Q-learning. Experimental evaluation of the proposed improved Q-learning under the challenging environment with a different layout of obstacles shows that the convergence of Q-learning can be accelerated when Q-values are initialized appropriately using the FPA. Additionally, the effectiveness of the proposed algorithm is validated in a real-world experiment using a three-wheeled mobile robot. |
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