Portrait of Caro Strickland

Caro Strickland

caro.strickland@mila.quebec
Postdoctorate - Université Laval
Supervisor
Audrey Durand
Research Topics
Deep Learning
Drug Discovery
GFlowNets
Reinforcement Learning
Website
Google Scholar
GitHub

Publications

What are we measuring? A critical examination of MO-MuJoCo and evaluation consistency in continuous multi-objective reinforcement learning
Baptiste Bonin
Caro Strickland
Audrey Durand
Although Multi-Objective Reinforcement Learning (MORL) research relies heavily on MO-MuJoCo as its go-to continuous control benchmark, the v… (see more)alidity of the conclusions drawn from it remain underexamined. In this paper, we first discuss three structural limitations of MO-MuJoCo; 1) its objectives are decomposed from pre-existing scalar rewards rather than independently motivated goals; 2) environments repeat the same underlying trade-off structure across varied locomotion morphologies, providing \textit{surface variety} without genuine \textit{problem diversity}; and 3) empirically approximated Pareto fronts appear broadly convex across research, potentially failing to stress-test the limitations of scalarization-based techniques. Setting these concerns aside, we further demonstrate that algorithmic rankings under MO-MuJoCo are highly sensitive to often undocumented evaluation choices in research papers. Across five evaluation axes, including reference point selection, weight distribution, normalization, return type, and front extraction method, pairwise algorithm rankings reverse in up to 47\% of configurations. Variance decomposition reveals that normalization alone accounts for nearly 69\% of hypervolume variance, suppressing the algorithm impact. Ultimately, we argue that progress in MORL research requires not only increased scrutiny of the benchmarks we rely upon, but also greater clarity in how results obtained within them are reported.
2026-08-14
Finding the Frame @ Reinforcement Learning Conference (published)
openreview.net
openreview.net