no code implementations • 11 Feb 2024 • Willem Röpke, Mathieu Reymond, Patrick Mannion, Diederik M. Roijers, Ann Nowé, Roxana Rădulescu
A significant challenge in multi-objective reinforcement learning is obtaining a Pareto front of policies that attain optimal performance under different preferences.
Multi-Objective Reinforcement Learning reinforcement-learning
no code implementations • 5 Feb 2024 • Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel Ramos, Roxana Rădulescu, Willem Röpke, Diederik M. Roijers
Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the utility derived by the user from those rewards.
Multi-Objective Reinforcement Learning reinforcement-learning
no code implementations • 23 Jan 2024 • Nicole Orzan, Erman Acar, Davide Grossi, Roxana Rădulescu
Understanding the emergence of cooperation in systems of computational agents is crucial for the development of effective cooperative AI.
no code implementations • 11 Apr 2022 • Mathieu Reymond, Conor F. Hayes, Lander Willem, Roxana Rădulescu, Steven Abrams, Diederik M. Roijers, Enda Howley, Patrick Mannion, Niel Hens, Ann Nowé, Pieter Libin
As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strategies in combination with complex epidemic models.
1 code implementation • 17 Nov 2021 • Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Rădulescu
We consider preference communication in two-player multi-objective normal-form games.
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 • 14 Nov 2020 • Roxana Rădulescu, Timothy Verstraeten, Yijie Zhang, Patrick Mannion, Diederik M. Roijers, Ann Nowé
We contribute novel actor-critic and policy gradient formulations to allow reinforcement learning of mixed strategies in this setting, along with extensions that incorporate opponent policy reconstruction and learning with opponent learning awareness (i. e., learning while considering the impact of one's policy when anticipating the opponent's learning step).
no code implementations • 17 Jan 2020 • Roxana Rădulescu, Patrick Mannion, Yijie Zhang, Diederik M. Roijers, Ann Nowé
In multi-objective multi-agent systems (MOMAS), agents explicitly consider the possible tradeoffs between conflicting objective functions.
no code implementations • 6 Sep 2019 • Roxana Rădulescu, Patrick Mannion, Diederik M. Roijers, Ann Nowé
We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied.
no code implementations • 28 Feb 2017 • Roxana Rădulescu, Peter Vrancx, Ann Nowé
Congestion problems are omnipresent in today's complex networks and represent a challenge in many research domains.
Multi-agent Reinforcement Learning reinforcement-learning +1