4 citations · 6 across the 5 of their papers we have counts for
5 papers
Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization
Yufei Kuang, Miao Lu, Jie Wang +3
Deep reinforcement learning algorithms can perform poorly in real-world tasks due to the discrepancy between source and target environments. This discrepancy is commonly viewed as…
Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic
Zhihai Wang, Jie Wang, Qi Zhou +2
Model-based reinforcement learning algorithms, which aim to learn a model of the environment to make decisions, are more sample efficient than their model-free counterparts. The sa…
Optimal Power Control for DoS Attack over Fading Channel: A Game-Theoretic Approach
Jie Wang, Jiahu Qin, Menglin Li +1
In this paper, we investigate remote state estimation against an intelligent denial-of-service (DoS) attack over a vulnerable wireless network whose channel undergoes attenuation a…
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization
Qi Zhou, Houqiang Li, Jie Wang
Model-based reinforcement learning algorithms tend to achieve higher sample efficiency than model-free methods. However, due to the inevitable errors of learned models, model-based…
Randomized Consensus based Distributed Kalman Filtering over Wireless Sensor Networks
Jiahu Qin, Jie Wang, Ling Shi +1
This paper is concerned with developing a novel distributed Kalman filtering algorithm over wireless sensor networks based on randomized consensus strategy. Compared with the centr…