222 citations · 600 across the 46 of their papers we have counts for
8 papers · 1 filter
Computing Necessary Conditions for Near-Optimality in Capacity Expansion Planning Problems
Antoine Dubois, Damien Ernst
In power systems, large-scale optimisation problems are extensively used to plan for capacity expansion at the supra-national level. However, their cost-optimal solutions are often…
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
Thibaut Théate, Antoine Wehenkel, Adrien Bolland +2
The distributional reinforcement learning (RL) approach advocates for representing the complete probability distribution of the random return instead of only modelling its expectat…
Warming up recurrent neural networks to maximise reachable multistability greatly improves learning
Gaspard Lambrechts, Florent De Geeter, Nicolas Vecoven +2
Training recurrent neural networks is known to be difficult when time dependencies become long. In this work, we show that most standard cells only have one stable equilibrium at i…
Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education
Robin Henry, Damien Ernst
Gym-ANM is a Python package that facilitates the design of reinforcement learning (RL) environments that model active network management (ANM) tasks in electricity networks. Here,…
Model Reduction in Capacity Expansion Planning Problems via Renewable Generation Site Selection
David Radu, Antoine Dubois, Mathias Berger +1
The accurate representation of variable renewable generation (RES, e.g., wind, solar PV) assets in capacity expansion planning (CEP) studies is paramount to capture spatial and tem…
Sparse Training Theory for Scalable and Efficient Agents
Decebal Constantin Mocanu, Elena Mocanu, Tiago Pinto +5
A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinf…