26 citations · 29 across the 3 of their papers we have counts for
3 papers
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…
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…
Learning to predict where to look in interactive environments using deep recurrent q-learning
Sajad Mousavi, Michael Schukat, Enda Howley +2
Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e.g., sandwich making and playing the video games…