Learning control of underactuated double pendulum with Model-Based Reinforcement Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866914944547553280 |
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| author | Turcato, Niccolò Libera, Alberto Dalla Giacomuzzo, Giulio Carli, Ruggero Romeres, Diego |
| author_facet | Turcato, Niccolò Libera, Alberto Dalla Giacomuzzo, Giulio Carli, Ruggero Romeres, Diego |
| contents | This report describes our proposed solution for the second AI Olympics competition held at IROS 2024. Our solution is based on a recent Model-Based Reinforcement Learning algorithm named MC-PILCO. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the tasks at hand. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05811 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning control of underactuated double pendulum with Model-Based Reinforcement Learning Turcato, Niccolò Libera, Alberto Dalla Giacomuzzo, Giulio Carli, Ruggero Romeres, Diego Robotics This report describes our proposed solution for the second AI Olympics competition held at IROS 2024. Our solution is based on a recent Model-Based Reinforcement Learning algorithm named MC-PILCO. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the tasks at hand. |
| title | Learning control of underactuated double pendulum with Model-Based Reinforcement Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.05811 |