9 citations · 12 across the 3 of their papers we have counts for
9 papers
Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture
Chin-Jui Chang, Yu-Wei Chu, Chao-Hsien Ting +3
Deep reinforcement learning (DRL) has been demonstrated to provide promising results in several challenging decision making and control tasks. However, the required inference costs…
Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation
Chen-Hao Chao, Bo-Wun Cheng, Chun-Yi Lee
Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks. Nevertheless, t…
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…
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…
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…
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…