447 citations · 857 across the 37 of their papers we have counts for
3 papers · 1 filter
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…