6 citations · 10 across the 16 of their papers we have counts for
17 papers
Reinformed Dreamer: An Asymmetric World Model Efficiently Trained through Latent Guidance
Gaspard Lambrechts, Adrien Bolland, Daniel Ebi +1
Much like humans benefit from guidance while learning, reinforcement learning algorithms may benefit from additional supervision beyond rewards. Leveraging additional information d…
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…
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…
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…
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…
Cost Estimation in Unit Commitment Problems Using Simulation-Based Inference
Matthias Pirlet, Adrien Bolland, Gilles Louppe +1
The Unit Commitment (UC) problem is a key optimization task in power systems to forecast the generation schedules of power units over a finite time period by minimizing costs while…
Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems
Marine Cauz, Adrien Bolland, Nicolas Wyrsch +1
The ongoing energy transition drives the development of decentralised renewable energy sources, which are heterogeneous and weather-dependent, complicating their integration into e…
Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms
Samy Aittahar, Adrien Bolland, Guillaume Derval +1
We propose in this paper an optimal control framework for renewable energy communities (RECs) equipped with controllable assets. Such RECs allow its members to exchange production…
Behind the Myth of Exploration in Policy Gradients
Adrien Bolland, Gaspard Lambrechts, Damien Ernst
In order to compute near-optimal policies with policy-gradient algorithms, it is common in practice to include intrinsic exploration terms in the learning objective. Although the e…
Reinforcement Learning for Joint Design and Control of Battery-PV Systems
Marine Cauz, Adrien Bolland, Bardhyl Miftari +3
The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have em…
Informed POMDP: Leveraging Additional Information in Model-Based RL
Gaspard Lambrechts, Adrien Bolland, Damien Ernst
In this work, we generalize the problem of learning through interaction in a POMDP by accounting for eventual additional information available at training time. First, we introduce…
Policy Gradient Algorithms Implicitly Optimize by Continuation
Adrien Bolland, Gilles Louppe, Damien Ernst
Direct policy optimization in reinforcement learning is usually solved with policy-gradient algorithms, which optimize policy parameters via stochastic gradient ascent. This paper…
Recurrent networks, hidden states and beliefs in partially observable environments
Gaspard Lambrechts, Adrien Bolland, Damien Ernst
Reinforcement learning aims to learn optimal policies from interaction with environments whose dynamics are unknown. Many methods rely on the approximation of a value function to d…
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…
Jointly Learning Environments and Control Policies with Projected Stochastic Gradient Ascent
Adrien Bolland, Ioannis Boukas, Mathias Berger +1
We consider the joint design and control of discrete-time stochastic dynamical systems over a finite time horizon. We formulate the problem as a multi-step optimization problem und…
A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding
Ioannis Boukas, Damien Ernst, Thibaut Théate +5
The large integration of variable energy resources is expected to shift a large part of the energy exchanges closer to real-time, where more accurate forecasts are available. In th…