17 citations · 18 across the 4 of their papers we have counts for
4 papers
Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning
Willem Röpke, Mathieu Reymond, Patrick Mannion +3
An important challenge in multi-objective reinforcement learning is obtaining a Pareto front of policies to attain optimal performance under different preferences. We introduce Ite…
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…
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
Denis Steckelmacher, Diederik M. Roijers, Anna Harutyunyan +3
Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability. While hierarchy and partial observability are us…
Solving stable matching problems using answer set programming
Sofie De Clercq, Steven Schockaert, Martine De Cock +1
Since the introduction of the stable marriage problem (SMP) by Gale and Shapley (1962), several variants and extensions have been investigated. While this variety is useful to wide…