222 citations · 600 across the 46 of their papers we have counts for
4 papers · 2 filters
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,…
Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems
Robin Henry, Damien Ernst
Active network management (ANM) of electricity distribution networks include many complex stochastic sequential optimization problems. These problems need to be solved for integrat…