4 papers
Gradient-based Regularization for Action Smoothness in Robotic Control with Reinforcement Learning
I Lee, Hoang-Giang Cao, Cong-Tinh Dao +2
Deep Reinforcement Learning (DRL) has achieved remarkable success, ranging from complex computer games to real-world applications, showing the potential for intelligent agents capa…
Point Cloud Models Improve Visual Robustness in Robotic Learners
Skand Peri, Iain Lee, Chanho Kim +3
Visual control policies can encounter significant performance degradation when visual conditions like lighting or camera position differ from those seen during training -- often ex…
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…
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…