1 code implementation • 11 Apr 2022 • Mathieu Reymond, Eugenio Bargiacchi, Ann Nowé
In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is an expensive process.
1 code implementation • 17 Mar 2021 • Conor F. Hayes, Roxana Rădulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives.
1 code implementation • 19 Jan 2021 • Timothy Verstraeten, Pieter-Jan Daems, Eugenio Bargiacchi, Diederik M. Roijers, Pieter J. K. Libin, Jan Helsen
This is a non-trivial optimization problem, as complex dependencies exist between the wind turbines.
no code implementations • 15 Jan 2020 • Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé
We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes.
Model-based Reinforcement Learning Multi-agent Reinforcement Learning +3
1 code implementation • 22 Nov 2019 • Timothy Verstraeten, Eugenio Bargiacchi, Pieter JK Libin, Jan Helsen, Diederik M. Roijers, Ann Nowé
In this task, wind turbines must coordinate their alignments with respect to the incoming wind vector in order to optimize power production.
no code implementations • ICML 2018 • Eugenio Bargiacchi, Timothy Verstraeten, Diederik Roijers, Ann Nowé, Hado Hasselt
Learning to coordinate between multiple agents is an important problem in many reinforcement learning problems.