Comparison of Model-Based and Model-Free Reinforcement Learning for Real-World Dexterous Robotic Manipulation Tasks
Author:
Affiliation:
1. The University of Auckland,Centre for Automation and Robotic Engineering Science,New Zealand
2. New Dexterity Research Group, The University of Auckland,New Zealand
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10160211/10160212/10160983.pdf?arnumber=10160983
Reference53 articles.
1. Push-grasping with dexterous hands: Mechanics and a method
2. Average-reward model-free reinforcement learning: a systematic review and literature mapping;dewanto;ar Xiv preprint,2020
3. Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
4. Addressing function approximation error in actor-critic methods;fujimoto;International Conference on Machine Learning,0
5. Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost
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