Hierarchical Behaviour Spaces

• URL: http://arxiv.org/abs/2604.24558v1
• Date: Mon, 27 Apr 2026 14:47:22 GMT
• Title: Hierarchical Behaviour Spaces
• Abstract summary: 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.<n>We call this method Hierarchical Behaviour Spaces (HBS)
• Score: 18.011776735232576
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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