Deep deterministic policy gradient - model-agnostic meta-learning framework: Efficient adaptation in continuous control tasks

Deep reinforcement learning (DRL) demonstrates superior performance in continuous control tasks. However, extensive training across a variety of environments is frequently necessitates extensive training. This manuscript presents Meta-DDPG-MAML, which combines the Deep Deterministic Policy Gradient...

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Bibliographic Details
Main Authors:	Ebrahim Hamid Sumiea, Said Jadid Abdulkadir, Hitham Seddig Alhussian, Safwan Mahmood Al-Selwi, Alawi Alqushaibi, Mohammed Gamal Ragab, Abdul Muiz Fayyaz
Format:	Article
Language:	English
Published:	Elsevier 2025-06-01
Series:	Results in Engineering
Subjects:	Deep deterministic policy gradient Deep reinforcement learning Model-agnostic meta-learning Continuous control tasks Hyperparameters Optimization
Online Access:	http://www.sciencedirect.com/science/article/pii/S2590123025012149
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Deep deterministic policy gradient - model-agnostic meta-learning framework: Efficient adaptation in continuous control tasks

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