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Journal of Machine Learning Research, ISSN 1532-4435, 09/2016, Volume 17, pp. 1 - 66
Journal Article
Journal of Artificial Intelligence Research, ISSN 1076-9757, 10/2016, Volume 57, pp. 187 - 227
Many real-world control applications, from economics to robotics, are characterized by the presence of multiple conflicting objectives. In these problems, the... 
COMPROMISE SOLUTIONS | OPTIMIZATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | FITTED-Q-ITERATION | SETS
Journal Article
IEEE Transactions on Smart Grid, ISSN 1949-3053, 09/2017, Volume 8, Issue 5, pp. 2149 - 2159
Journal Article
Water Resources Research, ISSN 0043-1397, 06/2013, Volume 49, Issue 6, pp. 3476 - 3486
The operation of large‐scale water resources systems often involves several conflicting and noncommensurable objectives. The full characterization of tradeoffs... 
multiobjective optimization | reservoir operation | reinforcement learning | ENVIRONMENTAL SCIENCES | FITTED-Q-ITERATION | WATER RESOURCES | OPTIMIZATION | LIMNOLOGY | Case studies | Objectives | Models | Algorithms | Optimization | Mathematical programming
Journal Article
Artificial Intelligence in Medicine, ISSN 0933-3657, 2014, Volume 62, Issue 1, pp. 47 - 60
Journal Article
Journal of Web Engineering, ISSN 1540-9589, 03/2017, Volume 16, Issue 1-2, pp. 126 - 144
There is a demand for web intelligence in e-business and internet oriented markets. Many data crunching tools are available for the vendors to predict the... 
Neural fitted Q – iteration | Weblog | Browsing pattern | Machine learning | Reinforcement learning | Web mining | Web page classification | COMPUTER SCIENCE, SOFTWARE ENGINEERING | Neural fitted Q - Iteration | COMPUTER SCIENCE, THEORY & METHODS
Journal Article
Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, ISSN 1548-8403, 2015, Volume 1, pp. 163 - 170
Conference Proceeding
Transportation Research Part C, ISSN 0968-090X, 08/2018, Volume 93, pp. 179 - 197
•Model-based methods might be impractical in solving the adaptive routing problem.•Reinforcement learning is an effective non-parametric model-free method to... 
Adaptive routing | Q learning | Reinforcement learning | Fitted Q iteration | Tree-based function approximation | SHORTEST-PATH | TRAFFIC INFORMATION | TRANSPORTATION SCIENCE & TECHNOLOGY | REAL-TIME | Case studies | Management science | Algorithms | Analysis | Green technology
Journal Article
2016 International Joint Conference on Neural Networks (IJCNN), 07/2016, Volume 2016-, pp. 4539 - 4544
Conference Proceeding
Intelligent Decision Technologies, ISSN 1872-4981, 2017, Volume 11, Issue 2, pp. 167 - 175
Reinforcement learning (RL) concerns algorithms tasked with learning optimal control policies by interacting with or observing a system. In computer science... 
marginalized transition models | Fitted Q-iteration | Decision theory | nonparametric | sample size | reinforcement learning | Medical research | Researchers | Policies | Optimal control | Machine learning | Sampling methods | Disease control
Journal Article
2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 09/2019, pp. 1 - 5
Motivated by the increasing interest in the application of machine learning techniques for power system control and demand response applications, this paper... 
microgrids | fitted q-iteration | regression | reinforcement learning
Conference Proceeding
2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 10/2018, pp. 1 - 6
Motivated by the recent developments in machine learning and artificial intelligence, this work contributes to the application of reinforcement learning in... 
Uncertainty | flexibility | Heat pumps | Production | Batteries | Smart grids | fitted Q-iteration | Resistance heating | reinforcement learning
Conference Proceeding
Sustainable Energy, Grids and Networks, ISSN 2352-4677, 06/2016, Volume 6, pp. 81 - 90
Driven by the opportunity to harvest the flexibility related to building climate control for demand response applications, this work presents a data-driven... 
Data-driven modeling | Fitted Q-iteration | Batch reinforcement learning | Demand response | Thermostatically controlled load | ENERGY & FUELS | MODEL-PREDICTIVE CONTROL | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
2018 IEEE International Energy Conference (ENERGYCON), 06/2018, pp. 1 - 6
As batch reinforcement learning algorithms reach maturity and neural networks are used increasingly in reinforcement learning, a performance comparison of... 
Heat pumps | Computational modeling | Neural networks | Buildings | Computer architecture | Approximation algorithms | residential demand response | fitted Q-iteration | Load modeling | reinforcement learning | Fitted Q-iteration | Residential demand response | Reinforcement learning
Conference Proceeding
2014 Power Systems Computation Conference, 08/2014, pp. 1 - 7
A demand response aggregator, that manages a large cluster of heterogeneous flexibility carriers, faces a complex optimal control problem. Moreover, in most... 
Temperature sensors | electric water heater | Electricity | Water heating | Learning (artificial intelligence) | Aggregator | batch reinforcement learning | Trajectory | fitted Q-iteration | Resistance heating | Load modeling | demand response
Conference Proceeding
2016 IEEE International Energy Conference (ENERGYCON), 04/2016, pp. 1 - 6
This paper demonstrates the application of a data-driven approach, based on fitted Q-iteration, in a living lab with an air conditioning unit and a... 
Air conditioning | Photovoltaic systems | Water heating | Approximation algorithms | Load management | Cost function | Air conditioning unit | fitted Q-iteration | demand response | reinforcement learning | Solar cells | Electric power generation | Stability | Computer simulation | Conferences | Photovoltaic cells | Electronics
Conference Proceeding
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