2 citations · 6 across the 4 of their papers we have counts for
4 papers
Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective
Nai-Chieh Huang, Ping-Chun Hsieh, Kuo-Hao Ho +4
Policy optimization is a fundamental principle for designing reinforcement learning algorithms, and one example is the proximal policy optimization algorithm with a clipped surroga…
Reinforcement Learning Trajectory Generation and Control for Aggressive Perching on Vertical Walls with Quadrotors
Chen-Huan Pi, Kai-Chun Hu, Yu-Ting Huang +1
Micro aerial vehicles are widely being researched and employed due to their relative low operation costs and high flexibility in various applications. We study the under-actuated q…
Towards Combining On-Off-Policy Methods for Real-World Applications
Kai-Chun Hu, Chen-Huan Pi, Ting Han Wei +4
In this paper, we point out a fundamental property of the objective in reinforcement learning, with which we can reformulate the policy gradient objective into a perceptron-like lo…
Learning a Representation Map for Robot Navigation using Deep Variational Autoencoder
Kaixin Hu, Peter O'Connor
The aim of this work is to use Variational Autoencoder (VAE) to learn a representation of an indoor environment that can be used for robot navigation. We use images extracted from…