Search Results for author: Ya-Chu Hsu

Found 2 papers, 1 papers with code

A selected review on reinforcement learning based control for autonomous underwater vehicles

no code implementations27 Nov 2019 Ya-Chu Hsu, Hui Wu, Keyou You, Shiji Song

This paper provides a selected review on RL based control for AUVs with the focus on applications of RL to low-level control tasks for underwater regulation and tracking.


Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning

1 code implementation NeurIPS 2019 Wenjie Shi, Shiji Song, Hui Wu, Ya-Chu Hsu, Cheng Wu, Gao Huang

To tackle this problem, we propose a general acceleration method for model-free, off-policy deep RL algorithms by drawing the idea underlying regularized Anderson acceleration (RAA), which is an effective approach to accelerating the solving of fixed point problems with perturbations.

reinforcement-learning Reinforcement Learning (RL)

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