Yuan-Hong Liao(Andrew)
Undergraduate [at] NTHU
andrewliao11 [at]
In Deep.
I'm fascinated with the causaility of reinforcement learning agent, safe RL, and uncertainty of deep neural policies. My ultimate goal is to learn an powerful agent and interpretable, safe to human beings at the same time..


  • Parameter space perturbation to help Exploration

    Recently, OpenAI has shown that applying spherical perturbation on parameter space provides an alternative for reinforcement learning[1]. On this month, DeepMind and OpenAI, both the xxx in RL, propose method using pertubation on parameter space for exploration[2,3]. Let’s see how this work and how it affect some off-the-shelf RL algorithms

  • Human-level control through deep reinforcement learning

    Here, I’ll talk about the red-hot topic: deep reinforcement learning. After the trend of the AlphaGo. Many people have been stunned by the smartness of the machine, which makes the topic of human versus artificial intelligence rises again. This blog will mainly focus on this work: Human-level control through deep reinforcement learning, published in Nature. And I’ll briefly talked about the recent released open source: gym, release by OpenAI

  • Video Object Detection using Faster R-CNN

    I implement Ross’s work ‘Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks’ to realize real-time object detection, which focuses on image-level problem. Here, I extend it to video-level problem by treating videos like a series of frames and also take the relation between each frame into account. Use a tracker to track the video frame by frame and finally visualize the final result.