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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| Автори: | , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
Taylor & Francis Group
2025-12-01
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| Серія: | Systems Science & Control Engineering |
| Предмети: | |
| Онлайн доступ: | https://www.tandfonline.com/doi/10.1080/21642583.2025.2498914 |
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