22 citations · 25 across the 6 of their papers we have counts for
6 papers
Towards Fully Automated Decision-Making Systems for Greenhouse Control: Challenges and Opportunities
Yongshuai Liu, Taeyeong Choi, Xin Liu
Machine learning has been successful in building control policies to drive a complex system to desired states in various applications (e.g. games, robotics, etc.). To be specific,…
Look Before Leap: Look-Ahead Planning with Uncertainty in Reinforcement Learning
Yongshuai Liu, Xin Liu
Model-based reinforcement learning (MBRL) has demonstrated superior sample efficiency compared to model-free reinforcement learning (MFRL). However, the presence of inaccurate mode…
Adventurer: Exploration with BiGAN for Deep Reinforcement Learning
Yongshuai Liu, Xin Liu
Recent developments in deep reinforcement learning have been very successful in learning complex, previously intractable problems. Sample efficiency and local optimality, however,…
CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing
Yongshuai Liu, Jiaxin Ding, Zhi-Li Zhang +1
As mobile networks proliferate, we are experiencing a strong diversification of services, which requires greater flexibility from the existing network. Network slicing is proposed…
IPO: Interior-point Policy Optimization under Constraints
Yongshuai Liu, Jiaxin Ding, Xin Liu
In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cum…
Less is More: Culling the Training Set to Improve Robustness of Deep Neural Networks
Yongshuai Liu, Jiyu Chen, Hao Chen
Deep neural networks are vulnerable to adversarial examples. Prior defenses attempted to make deep networks more robust by either changing the network architecture or augmenting th…