A Boolean Task Algebra for Reinforcement Learning
- Nangue Tasse, Geraud*; James, Steven D; Rosman, Benjamin
- Spotlight talk
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Poster session from 15:00 to 16:00 EAT and from 20:45 to 21:45 EAT
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We propose a framework for defining a Boolean algebra over the space of tasks. This allows us to formulate new tasks in terms of the negation, disjunction, and conjunction of a set of base tasks. We then show that by learning goal-oriented value functions and restricting the transition dynamics of the tasks, an agent can solve these new tasks with no further learning. We prove that by composing these value functions in specific ways, we immediately recover the optimal policies for all tasks expressible under the Boolean algebra.