18 citations · 33 across the 6 of their papers we have counts for
6 papers
Analysis and Optimisation of Bellman Residual Errors with Neural Function Approximation
Martin Gottwald, Sven Gronauer, Hao Shen +1
Recent development of Deep Reinforcement Learning (DRL) has demonstrated superior performance of neural networks in solving challenging problems with large or even continuous state…
Attentional Separation-and-Aggregation Network for Self-supervised Depth-Pose Learning in Dynamic Scenes
Feng Gao, Jincheng Yu, Hao Shen +2
Learning depth and ego-motion from unlabeled videos via self-supervision from epipolar projection can improve the robustness and accuracy of the 3D perception and localization of v…
Reinforcement Learning in Conflicting Environments for Autonomous Vehicles
Dominik Meyer, Johannes Feldmaier, Hao Shen
In this work, we investigate the application of Reinforcement Learning to two well known decision dilemmas, namely Newcomb's Problem and Prisoner's Dilemma. These problems are exem…
Regularized Gradient Temporal-Difference Learning
Dominik Meyer, Hao Shen, Klaus Diepold
In this paper, we study the Temporal Difference (TD) learning with linear value function approximation. It is well known that most TD learning algorithms are unstable with linear f…
Optimal Water Heater Control in Smart Home Environments
Christoph Passenberg, Dominik Meyer, Johannes Feldmaier +1
In this work, we develop an optimal water heater control method for a smart home environment. It is important to notice that unlike battery storage systems, energy flow in a water…
Monitoring and Prediction in Smart Energy Systems via Multi-timescale Nexting
Johannes Feldmaier, Dominik Meyer, Hao Shen +1
Reliable prediction of system status is a highly demanded functionality of smart energy systems, which can enable users or human operators to react quickly to potential future syst…