4 citations · 8 across the 7 of their papers we have counts for
7 papers
Image-based Regularization for Action Smoothness in Autonomous Miniature Racing Car with Deep Reinforcement Learning
Hoang-Giang Cao, I Lee, Bo-Jiun Hsu +4
Deep reinforcement learning has achieved significant results in low-level controlling tasks. However, for some applications like autonomous driving and drone flying, it is difficul…
Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors
Hoang-Giang Cao, Weihao Zeng, I-Chen Wu
Picking cluttered general objects is a challenging task due to the complex geometries and various stacking configurations. Many prior works utilize pose estimation for picking, but…
Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation
Hoang-Giang Cao, Weihao Zeng, I-Chen Wu
It is crucial to address the following issues for ubiquitous robotics manipulation applications: (a) vision-based manipulation tasks require the robot to visually learn and underst…
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
Li-Cheng Lan, Huan Zhang, Ti-Rong Wu +3
The success of AlphaZero (AZ) has demonstrated that neural-network-based Go AIs can surpass human performance by a large margin. Given that the state space of Go is extremely large…
Image-Based Conditioning for Action Policy Smoothness in Autonomous Miniature Car Racing with Reinforcement Learning
Bo-Jiun Hsu, Hoang-Giang Cao, I Lee +3
In recent years, deep reinforcement learning has achieved significant results in low-level controlling tasks. However, the problem of control smoothness has less attention. In auto…
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…
Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification
Bo Yang Hsueh, Wei Li, I-Chen Wu
Learning rate scheduler has been a critical issue in the deep neural network training. Several schedulers and methods have been proposed, including step decay scheduler, adaptive m…