Actor-critic reinforcement learning for tracking control in robotics

By Yudha Prawira Pane, Subramanya P. Nageshrao, Robert Babuška. 2016

25 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Caiming Xiong
2. Richard Socher
3. Mohit Bansal
4. Luke S. Zettlemoyer
5. Graham Neubig
6. Percy Liang
7. Jianfeng Gao
8. Yejin Choi

In Control and Systems Engineering

1. Alexander R. Trott
2. Richard Socher
3. Caiming Xiong
4. Stephan Zheng
5. Wenling Shang

Citations in the corpus

• Learning World Graphs to Accelerate Hierarchical Reinforcement Learning (2019, positive): “Advantage Actor-Critic (A2C), is a popular and relatively simple-touse, model-free, on-policy RL algorithm (Wu & Tian, 2016; Pane et al., 2016; Mnih et al., 2016).”