93 citations · 97 across the 6 of their papers we have counts for
6 papers
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…
What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization
Zuzanna Osika, Jazmin Zatarain Salazar, Diederik M. Roijers +2
We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse…
Bridging the Gap Between Single and Multi Objective Games
Willem Röpke, Carla Groenland, Roxana Rădulescu +2
A classic model to study strategic decision making in multi-agent systems is the normal-form game. This model can be generalised to allow for an infinite number of pure strategies…
Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models
Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers +2
Many real-world problems contain multiple objectives and agents, where a trade-off exists between objectives. Key to solving such problems is to exploit sparse dependency structure…
Multi-Objective Deep Reinforcement Learning
Hossam Mossalam, Yannis M. Assael, Diederik M. Roijers +1
We propose Deep Optimistic Linear Support Learning (DOL) to solve high-dimensional multi-objective decision problems where the relative importances of the objectives are not known…