Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning

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

Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective 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

Distributional Multi-Objective Decision Making

1 code implementation • 9 May 2023 • Willem Röpke, Conor F. Hayes, Patrick Mannion, Enda Howley, Ann Nowé, Diederik M. Roijers

For effective decision support in scenarios with conflicting objectives, sets of potentially optimal solutions can be presented to the decision maker.

Decision Making

Preference Communication in Multi-Objective Normal-Form Games

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.