b'Ann Now\xc3\xa9'

research

∙ 06/16/2023

Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth

Communication plays a vital role in multi-agent systems, fostering colla...

0 Qingshuang Sun, et al. ∙

research

∙ 05/09/2023

Distributional Multi-Objective Decision Making

For effective decision support in scenarios with conflicting objectives,...

0 Willem Röpke, et al. ∙

research

∙ 05/02/2023

Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making

Experts advising decision-makers are likely to display expertise which v...

0 Axel Abels, et al. ∙

research

∙ 04/18/2023

Safe reinforcement learning with self-improving hard constraints for multi-energy management systems

Safe reinforcement learning (RL) with hard constraint guarantees is a pr...

0 Glenn Ceusters, et al. ∙

research

∙ 03/22/2023

Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees

Although deep reinforcement learning (DRL) has many success stories, the...

0 Florent Delgrange, et al. ∙

research

∙ 03/06/2023

The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models

Partially Observable Markov Decision Processes (POMDPs) are useful tools...

0 Raphaël Avalos, et al. ∙

research

∙ 01/30/2023

Evaluating COVID-19 vaccine allocation policies using Bayesian m-top exploration

Individual-based epidemiological models support the study of fine-graine...

0 Alexandra Cimpean, et al. ∙

research

∙ 01/30/2023

Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem

Many instances of similar or almost-identical industrial machines or too...

0 Hélène Plisnier, et al. ∙

research

∙ 01/18/2023

Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization

Multi-objective reinforcement learning (MORL) algorithms tackle sequenti...

0 Lucas N. Alegre, et al. ∙

research

∙ 01/13/2023

Bridging the Gap Between Single and Multi Objective Games

A classic model to study strategic decision making in multi-agent system...

0 Willem Röpke, et al. ∙

research

∙ 07/08/2022

Safe reinforcement learning for multi-energy management systems with known constraint functions

Reinforcement learning (RL) is a promising optimal control technique for...

0 Glenn Ceusters, et al. ∙

research

∙ 04/11/2022

Pareto Conditioned Networks

In multi-objective optimization, learning all the policies that reach Pa...

0 Mathieu Reymond, et al. ∙

research

∙ 04/11/2022

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

Infectious disease outbreaks can have a disruptive impact on public heal...

31 Mathieu Reymond, et al. ∙

research

∙ 12/23/2021

Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) enables us to create adaptive ...

4 Raphaël Avalos, et al. ∙

research

∙ 12/17/2021

Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes (Technical Report)

We consider the challenge of policy simplification and verification in t...

0 Florent Delgrange, et al. ∙

research

∙ 12/13/2021

On Nash Equilibria in Normal-Form Games With Vectorial Payoffs

We provide an in-depth study of Nash equilibria in multi-objective norma...

0 Willem Röpke, et al. ∙

research

∙ 11/17/2021

Preference Communication in Multi-Objective Normal-Form Games

We study the problem of multiple agents learning concurrently in a multi...

2 Willem Röpke, et al. ∙

research

∙ 06/25/2021

Dealing with Expert Bias in Collective Decision-Making

Quite some real-world problems can be formulated as decision-making prob...

0 Axel Abels, et al. ∙

research

∙ 06/10/2021

Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning

Today's advanced Reinforcement Learning algorithms produce black-box pol...

0 Youri Coppens, et al. ∙

research

∙ 04/20/2021

Model-predictive control and reinforcement learning in multi-energy system case studies

Model-predictive-control (MPC) offers an optimal control technique to es...

0 Glenn Ceusters, et al. ∙

research

∙ 03/17/2021

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Real-world decision-making tasks are generally complex, requiring trade-...

55 Conor F. Hayes, et al. ∙

research

∙ 11/14/2020

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

Many real-world multi-agent interactions consider multiple distinct crit...

2 Roxana Rădulescu, et al. ∙

research

∙ 03/30/2020

Deep reinforcement learning for large-scale epidemic control

Epidemics of infectious diseases are an important threat to public healt...

29 Pieter Libin, et al. ∙

research

∙ 01/26/2020

An interpretable semi-supervised classifier using two different strategies for amended self-labeling

In the context of some machine learning applications, obtaining data ins...

23 Isel Grau, et al. ∙

research

∙ 01/17/2020

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

In multi-objective multi-agent systems (MOMAS), agents explicitly consid...

0 Roxana Rădulescu, et al. ∙

research

∙ 01/15/2020

Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping

We present a new model-based reinforcement learning algorithm, Cooperati...

0 Eugenio Bargiacchi, et al. ∙

research

∙ 11/22/2019

Fleet Control using Coregionalized Gaussian Process Policy Iteration

In many settings, as for example wind farms, multiple machines are insta...

0 Timothy Verstraeten, et al. ∙

research

∙ 11/22/2019

Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures

Multi-agent coordination is prevalent in many real-world applications. H...

0 Timothy Verstraeten, et al. ∙

research

∙ 11/22/2019

Thompson Sampling for Factored Multi-Agent Bandits

Multi-agent coordination is prevalent in many real-world applications. H...

0 Timothy Verstraeten, et al. ∙

research

∙ 09/30/2019

IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers

Over the last decade, the demand for better segmentation and classificat...

25 Felipe Gomez Marulanda, et al. ∙

research

∙ 09/06/2019

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

The majority of multi-agent system (MAS) implementations aim to optimise...

0 Roxana Rădulescu, et al. ∙

research

∙ 07/18/2019

Transfer Learning Across Simulated Robots With Different Sensors

For a robot to learn a good policy, it often requires expensive equipmen...

0 Hélène Plisnier, et al. ∙

research

∙ 03/11/2019

Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics

Value-based reinforcement-learning algorithms are currently state-of-the...

0 Denis Steckelmacher, et al. ∙

research

∙ 02/07/2019

The Actor-Advisor: Policy Gradient With Off-Policy Advice

Actor-critic algorithms learn an explicit policy (actor), and an accompa...

0 Hélène Plisnier, et al. ∙

research

∙ 09/20/2018

Dynamic Weights in Multi-Objective Deep Reinforcement Learning

Many real-world decision problems are characterized by multiple objectiv...

0 Axel Abels, et al. ∙

research

∙ 08/13/2018

Directed Policy Gradient for Safe Reinforcement Learning with Human Advice

Many currently deployed Reinforcement Learning agents work in an environ...

0 Hélène Plisnier, et al. ∙

research

∙ 02/21/2018

Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making

In multi-objective decision planning and learning, much attention is pai...

0 Luisa M Zintgraf, et al. ∙

research

∙ 11/16/2017

Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies

Pandemic influenza has the epidemic potential to kill millions of people...

0 Pieter Libin, et al. ∙

research

∙ 11/10/2017

Learning with Options that Terminate Off-Policy

A temporally abstract action, or an option, is specified by a policy and...

0 Anna Harutyunyan, et al. ∙

research

∙ 08/22/2017

Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets

Many real-world reinforcement learning problems have a hierarchical natu...

0 Denis Steckelmacher, et al. ∙

research

∙ 02/28/2017

Analysing Congestion Problems in Multi-agent Reinforcement Learning

Congestion problems are omnipresent in today's complex networks and repr...

0 Roxana Rădulescu, et al. ∙

research

∙ 12/16/2015

Solving stable matching problems using answer set programming

Since the introduction of the stable marriage problem (SMP) by Gale and ...

0 Sofie De Clercq, et al. ∙

research

∙ 02/11/2015

Off-Policy Reward Shaping with Ensembles

Potential-based reward shaping (PBRS) is an effective and popular techni...

0 Anna Harutyunyan, et al. ∙

research

∙ 05/21/2014

Off-Policy Shaping Ensembles in Reinforcement Learning

Recent advances of gradient temporal-difference methods allow to learn o...

0 Anna Harutyunyan, et al. ∙

research

∙ 02/28/2013

Modeling Stable Matching Problems with Answer Set Programming

The Stable Marriage Problem (SMP) is a well-known matching problem first...

0 Sofie De Clercq, et al. ∙

Ann Nowé

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