Search Results for author: Roxana Rădulescu

Found 10 papers, 3 papers with code

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

no code implementations11 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 implementations5 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

Emergent Cooperation under Uncertain Incentive Alignment

no code implementations23 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.

Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning

no code implementations11 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.

Decision Making Multi-Objective Reinforcement Learning +1

Preference Communication in Multi-Objective Normal-Form Games

1 code implementation17 Nov 2021 Willem Röpke, Diederik M. Roijers, Ann Nowé, Roxana Rădulescu

We consider preference communication in two-player multi-objective normal-form games.

Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games

1 code implementation14 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).

A utility-based analysis of equilibria in multi-objective normal form games

no code implementations17 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.

Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey

no code implementations6 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.

Decision Making

Analysing Congestion Problems in Multi-agent Reinforcement Learning

no code implementations28 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

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