A multi-task deep reinforcement learning framework based on curriculum learning and policy distillation for quadruped robot motor skill training
Deep reinforcement learning (RL) approaches are increasingly prominent in the field of robotics due to their adaptive decision-making capability. However, developing a single RL agent capable of performing multiple continuous control tasks for quadruped robots remains challenging. In this paper, a m...
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| Главные авторы: | , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Taylor & Francis Group
2025-12-01
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| Серии: | Systems Science & Control Engineering |
| Предметы: | |
| Online-ссылка: | https://www.tandfonline.com/doi/10.1080/21642583.2025.2498914 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
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