A Goal-Set Characterization of Task Composition in the Boolean Task Algebra
The Boolean Task Algebra (BTA) provides a principled framework for zero-shot task composition in reinforcement learning by equipping goal-reaching tasks with Boolean operations. We revisit its structural assumptions and formalize a collapse in the space of optimal extended Q-value functions: in deterministic MDPs, every such function is fully determined by the universal and empty tasks. This makes the logarithmic set of base tasks proposed in the original BTA formulation redundant. Building on this observation, we introduce a goal-set-based composition method that performs logical operations on goal sets and reconstructs composed value functions by selecting slices from the universal and ...
| Authors | Eduardo Terrés-Caballero, Herke van Hoof |
|---|---|
| Primary category | cs.LG |
| Published | 2 June 2026 |
| Last updated at source | 4 October 2026 |
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