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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Bibliografische gegevens

Hoofdauteurs:	Liang Chen, Bo Shen, Jiale Hong
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	Taylor & Francis Group 2025-12-01
Reeks:	Systems Science & Control Engineering
Onderwerpen:	Reinforcement learning multi-task policy distillation curriculum quadrupedal robots
Online toegang:	https://www.tandfonline.com/doi/10.1080/21642583.2025.2498914
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