Antoine Lesage-Landry
RetourPublications
Cahiers du GERAD
mars 2022
Antoine Lesage-Landry, Félix Pellerin, Joshua A. Taylor et Duncan Callaway
We formulate a batch reinforcement learning-based demand response approach to prevent distribution network constraint violations in unknown grids. We use the...
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mars 2022
Antoine Lesage-Landry et Duncan Callaway
We formulate a batch reinforcement learning-based demand response approach to prevent distribution network constraint violations in unknown grids. We use the...
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jan. 2022
Antoine Lesage-Landry et Duncan Callaway
We formulate an efficient approximation for multi-agent batch reinforcement learning, the approximated multi-agent fitted Q iteration (AMAFQI). We present a ...
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déc. 2021
Antoine Lesage-Landry, Joshua A. Taylor et Duncan Callaway
IEEE Transactions on Automatic Control, 66(12), 6164–6170, 2021
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oct. 2021
Antoine Lesage-Landry, Joshua A. Taylor et Iman Shames
IEEE Transactions on Automatic Control, 66(10), 4866–4872, 2021
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Actes de conférence
fév. 2022
Batch Reinforcement Learning for Network-Safe Demand Response in Unknown Electric Grids
Antoine Lesage-Landry et Duncan Callaway
À paraître dans : 22nd Power Systems Computation Conference (PSCC 2022), Porto, Portugal, 2022
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déc. 2021
Multi-agent reinforcement learning for renewable integration in the electric power grid
Vincent Mai, Tianyu Zhang et Antoine Lesage-Landry
NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning, 2021
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oct. 2021
Fitted Q-iteration for network-safe demand response
Antoine Lesage-Landry et Duncan Callaway
2021 INFORMS Annual Meeting, Anaheim, USA, 2021
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