Hierarchical Behaviour Spaces

Michael Matthews, Pierluca D'Oro, Anssi Kanervisto, Scott Fujimoto, Jakob Nicolaus Foerster, Mikael Henaff; 7, 2026.

Presented at the Reinforcement Learning Conference (RLC), Montréal, Quebec, Canada, August 15–17, 2026.

Abstract

Recent work in hierarchical reinforcement learning has shown success in scaling to billions of timesteps when learning over a set of predefined option reward functions. We show that, instead of using a single reward function per option, the reward functions can be effectively used to induce a space of behaviours, by letting the controller specify linear combinations over reward functions, allowing a more expressive set of policies to be represented. We call this method Hierarchical Behaviour Spaces (HBS). We evaluate HBS on the NetHack Learning Environment, demonstrating strong performance. We conduct a series of experiments and determine that, perhaps going against conventional wisdom, the benefits of hierarchy in our method come from increased exploration rather than long term reasoning.

[abs][pdf]

BibTeX

@article{matthews2026hierarchical,
    title={Hierarchical Behaviour Spaces},
    author={Michael Matthews and Pierluca D'Oro and Anssi Kanervisto and Scott Fujimoto and Jakob Nicolaus Foerster and Mikael Henaff},
    journal={Reinforcement Learning Journal},
    volume={7},
    pages={},
    year={2026}
}