447 citations · 857 across the 37 of their papers we have counts for
8 papers · 1 filter
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…
On Nash Equilibria in Normal-Form Games With Vectorial Payoffs
Willem Röpke, Diederik M. Roijers, Ann Nowé +1
We provide an in-depth study of Nash equilibria in multi-objective normal form games (MONFGs), i.e., normal form games with vectorial payoffs. Taking a utility-based approach, we a…
Scalar reward is not enough: A response to Silver, Singh, Precup and Sutton (2021)
Peter Vamplew, Benjamin J. Smith, Johan Kallstrom +9
The recent paper `"Reward is Enough" by Silver, Singh, Precup and Sutton posits that the concept of reward maximisation is sufficient to underpin all intelligence, both natural and…
Preference Communication in Multi-Objective Normal-Form Games
Willem Röpke, Diederik M. Roijers, Ann Nowé +1
We consider preference communication in two-player multi-objective normal-form games. In such games, the payoffs resulting from joint actions are vector-valued. Taking a utility-ba…
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…
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Conor F. Hayes, Roxana Rădulescu, Eugenio Bargiacchi +15
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcemen…
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…