activity
20182024
most citedNever Forget: Balancing Exploration and Exploitation via Learning Optical Flow

9 citations · 12 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20203 cited

Mixture of Step Returns in Bootstrapped DQN

Po-Han Chiang, Hsuan-Kung Yang, Zhang-Wei Hong +1

The concept of utilizing multi-step returns for updating value functions has been adopted in deep reinforcement learning (DRL) for a number of years. Updating value functions with…

cs.LG2019

Flow-based Intrinsic Curiosity Module

Hsuan-Kung Yang, Po-Han Chiang, Min-Fong Hong +1

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module…

cs.LG20199 cited

Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow

Hsuan-Kung Yang, Po-Han Chiang, Kuan-Wei Ho +2

Exploration bonus derived from the novelty of the states in an environment has become a popular approach to motivate exploration for deep reinforcement learning agents in the past…

cs.CV2018

Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information

Hsuan-Kung Yang, An-Chieh Cheng, Kuan-Wei Ho +2

In this paper, we investigate the use of an unsupervised label clustering technique and demonstrate that it enables substantial improvements in visual relationship prediction accur…

cs.CV2018

Dynamic Video Segmentation Network

Yu-Syuan Xu, Tsu-Jui Fu, Hsuan-Kung Yang +1

In this paper, we present a detailed design of dynamic video segmentation network (DVSNet) for fast and efficient semantic video segmentation. DVSNet consists of two convolutional…