447 citations · 857 across the 37 of their papers we have counts for
18 papers · 1 filter
It's a matter of timescale: non-linear utility in successor features and multi-objective planning and learning
Liam P. H. Mertens, Lucas N. Alegre, Florent Delgrange +3
Time is of the essence when dealing with multiple reward signals and non-linear utility. In this paper we argue that the current main approaches in multi-objective RL (SER and ESR)…
Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance
Dimitris Michailidis, Willem Röpke, Diederik M. Roijers +2
Multi-Objective Reinforcement Learning (MORL) aims to learn a set of policies that optimize trade-offs between multiple, often conflicting objectives. MORL is computationally more…
Online Planning in POMDPs with State-Requests
Raphael Avalos, Eugenio Bargiacchi, Ann Nowé +2
In key real-world problems, full state information is sometimes available but only at a high cost, like activating precise yet energy-intensive sensors or consulting humans, thereb…
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
Peter Vamplew, Cameron Foale, Conor F. Hayes +9
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 u…
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models
Raphael Avalos, Florent Delgrange, Ann Nowé +2
Partially Observable Markov Decision Processes (POMDPs) are used to model environments where the full state cannot be perceived by an agent. As such the agent needs to reason takin…
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
Lucas N. Alegre, Ana L. C. Bazzan, Diederik M. Roijers +2
Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision problems where agents may have different preferences over (possibly conflicting) reward function…
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning
Mathieu Reymond, Conor F. Hayes, Lander Willem +8
Infectious disease outbreaks can have a disruptive impact on public health and societal processes. As decision making in the context of epidemic mitigation is hard, reinforcement l…
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning
Raphaël Avalos, Mathieu Reymond, Ann Nowé +1
Many recent successful off-policy multi-agent reinforcement learning (MARL) algorithms for cooperative partially observable environments focus on finding factorized value functions…
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making
Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers +2
In many real-world scenarios, the utility of a user is derived from the single execution of a policy. In this case, to apply multi-objective reinforcement learning, the expected ut…
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search
Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers +2
In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from the single execution of a policy. In these settings, making decision…
Scalable Optimization for Wind Farm Control using Coordination Graphs
Timothy Verstraeten, Pieter-Jan Daems, Eugenio Bargiacchi +3
Wind farms are a crucial driver toward the generation of ecological and renewable energy. Due to their rapid increase in capacity, contemporary wind farms need to adhere to strict…
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping
Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers +1
We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes. The algorithm allo…
Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures
Timothy Verstraeten, Eugenio Bargiacchi, Pieter JK Libin +3
Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this r…
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
Denis Steckelmacher, Hélène Plisnier, Diederik M. Roijers +1
Value-based reinforcement-learning algorithms provide state-of-the-art results in model-free discrete-action settings, and tend to outperform actor-critic algorithms. We argue that…
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
Axel Abels, Diederik M. Roijers, Tom Lenaerts +2
Many real-world decision problems are characterized by multiple conflicting objectives which must be balanced based on their relative importance. In the dynamic weights setting the…
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice
Hélène Plisnier, Denis Steckelmacher, Tim Brys +2
Many currently deployed Reinforcement Learning agents work in an environment shared with humans, be them co-workers, users or clients. It is desirable that these agents adjust to p…
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
Luisa M Zintgraf, Diederik M Roijers, Sjoerd Linders +2
In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profi…
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies
Pieter Libin, Timothy Verstraeten, Diederik M. Roijers +4
Pandemic influenza has the epidemic potential to kill millions of people. While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies th…