Adaptive Improved Q-Learning Path Planning Algorithm Based on Obstacle Learning Matrix and Artificial Potential Field
To address the issues of exploration imbalance and slow convergence speed in the Q-learning path planning algorithm, an adaptive improved Q-learning path planning algorithm based on an obstacle learning matrix and artificial potential field (APF) is proposed. First, an obstacle learning matrix is es...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/jece/3110053 |
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| Summary: | To address the issues of exploration imbalance and slow convergence speed in the Q-learning path planning algorithm, an adaptive improved Q-learning path planning algorithm based on an obstacle learning matrix and artificial potential field (APF) is proposed. First, an obstacle learning matrix is established to store the positions of obstacles encountered in each learning iteration, avoiding redundant learning of the same obstacle. Second, an adaptive exploration enhancement strategy is introduced by incorporating the concept of success rate. This strategy divides the decay process of the exploration rate into an exploration-dominant initial stage and an exploitation-dominant later stage. Finally, an APF-weighted action selection strategy is introduced, utilizing the guiding force generated by the APF to encourage the mobile robot to avoid obstacles more efficiently. Simulation results show that the proposed method can effectively reduce the number of iterations and time consumption in the optimization process, resulting in a smoother and more stable planned path. |
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| ISSN: | 2090-0155 |