From the 1 of 7 linked papers with an AI index.
7 papers
Reinformed Dreamer: An Asymmetric World Model Efficiently Trained through Latent Guidance
Gaspard Lambrechts, Adrien Bolland, Daniel Ebi +1
The paper introduces Reinforced Dreamer, an asymmetric model‑based reinforcement learning algorithm that uses latent guidance to improve representation learning from privileged inf…
Bayesian Inference for Estimating Generation Costs in Electricity Markets
Matthias Pirlet, Adrien Bolland, Alexandre Huynen +3
Estimating generation costs from observed electricity market data is essential for market simulation, strategic bidding, and system planning. To that end, we model the relationship…
Maximum-Entropy Exploration with Future State-Action Visitation Measures
Adrien Bolland, Gaspard Lambrechts, Damien Ernst
Maximum entropy reinforcement learning motivates agents to explore states and actions to maximize the entropy of some distribution, typically by providing additional intrinsic rewa…
Gym-TORAX: Open-source software for integrating reinforcement learning with plasma control simulators in tokamak research
Antoine Mouchamps, Arthur Malherbe, Adrien Bolland +1
This paper presents Gym-TORAX, a Python package enabling the implementation of Reinforcement Learning (RL) environments for simulating plasma dynamics and control in tokamaks. User…
SecuLEx: a Secure Limit Exchange Market for Dynamic Operating Envelopes
Maurizio Vassallo, Adrien Bolland, Alireza Bahmanyar +9
Distributed energy resources (DERs) are transforming power networks, challenging traditional operational methods, and requiring new coordination mechanisms. To address this challen…
Off-Policy Maximum Entropy RL with Future State and Action Visitation Measures
Adrien Bolland, Gaspard Lambrechts, Damien Ernst
Maximum entropy reinforcement learning integrates exploration into policy learning by providing additional intrinsic rewards proportional to the entropy of some distribution. In th…