Improved reinforcement learning algorithm for mobile robot path planning
In order to solve the problem that traditional Q-learning algorithm has a large number of invalid iterations in the early convergence stage of robot path planning, an improved reinforcement learning algorithm is proposed. Firstly, the gravitational potential field in the improved artificial potentia...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02030.pdf |
| _version_ | 1828194789924798464 |
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| author | Luo Teng |
| author_facet | Luo Teng |
| author_sort | Luo Teng |
| collection | DOAJ |
| description | In order to solve the problem that traditional Q-learning algorithm has a large number of invalid iterations in the early convergence stage of robot path planning, an improved reinforcement learning algorithm is proposed. Firstly, the gravitational potential field in the improved artificial potential field algorithm is introduced when the Q table is initialized to accelerate the convergence. Secondly, the Tent Chaotic Mapping algorithm is added to the initial state determination process of the algorithm, which allows the algorithm to explore the environment more fully. In addition, an ε-greed strategy with the number of iterations changing the ε value becomes the action selection strategy of the algorithm, which improves the performance of the algorithm. Finally, the grid map simulation results based on MATLAB show that the improved Q-learning algorithm has greatly reduced the path planning time and the number of non-convergence iterations compared with the traditional algorithm. |
| first_indexed | 2024-04-12T09:30:59Z |
| format | Article |
| id | doaj.art-1f8e4e2d3bb74d358338e6748471deb7 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-04-12T09:30:59Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-1f8e4e2d3bb74d358338e6748471deb72022-12-22T03:38:21ZengEDP SciencesITM Web of Conferences2271-20972022-01-01470203010.1051/itmconf/20224702030itmconf_cccar2022_02030Improved reinforcement learning algorithm for mobile robot path planningLuo Teng0School of Information and Control Engineering, Liaoning Petrochemical UniversityIn order to solve the problem that traditional Q-learning algorithm has a large number of invalid iterations in the early convergence stage of robot path planning, an improved reinforcement learning algorithm is proposed. Firstly, the gravitational potential field in the improved artificial potential field algorithm is introduced when the Q table is initialized to accelerate the convergence. Secondly, the Tent Chaotic Mapping algorithm is added to the initial state determination process of the algorithm, which allows the algorithm to explore the environment more fully. In addition, an ε-greed strategy with the number of iterations changing the ε value becomes the action selection strategy of the algorithm, which improves the performance of the algorithm. Finally, the grid map simulation results based on MATLAB show that the improved Q-learning algorithm has greatly reduced the path planning time and the number of non-convergence iterations compared with the traditional algorithm.https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02030.pdfreinforcement learninggreedy strategymobile robotpath planning |
| spellingShingle | Luo Teng Improved reinforcement learning algorithm for mobile robot path planning ITM Web of Conferences reinforcement learning greedy strategy mobile robot path planning |
| title | Improved reinforcement learning algorithm for mobile robot path planning |
| title_full | Improved reinforcement learning algorithm for mobile robot path planning |
| title_fullStr | Improved reinforcement learning algorithm for mobile robot path planning |
| title_full_unstemmed | Improved reinforcement learning algorithm for mobile robot path planning |
| title_short | Improved reinforcement learning algorithm for mobile robot path planning |
| title_sort | improved reinforcement learning algorithm for mobile robot path planning |
| topic | reinforcement learning greedy strategy mobile robot path planning |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2022/07/itmconf_cccar2022_02030.pdf |
| work_keys_str_mv | AT luoteng improvedreinforcementlearningalgorithmformobilerobotpathplanning |