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Reinforcement learning algorithms struggle on tasks with complex hierarchicaldependency structures. Humans and other intelligent agents do not waste timeassessing the utility of every high-level action in existence, but instead onlyconsider ones they deem possible in the first place. By focusing only on whatis feasible, or "afforded", at the present moment, an agent can spend more timeboth evaluating the utility of and acting on what matters. To this end, wepresent Hierarchical Affordance Learning (HAL), a method that learns a model ofhierarchical affordances in order to prune impossible subtasks for moreeffective learning. Existing works in hierarchical reinforcement learningprovide agents with structural representations of subtasks but are notaffordance-aware, and by grounding our definition of hierarchical affordancesin the present state, our approach is more flexible than the multitude ofapproaches that ground their subtask dependencies in a symbolic history. Whilethese logic-based methods often require complete knowledge of the subtaskhierarchy, our approach is able to utilize incomplete and varying symbolicspecifications. Furthermore, we demonstrate that relative tonon-affordance-aware methods, HAL agents are better able to efficiently learncomplex tasks, navigate environment stochasticity, and acquire diverse skillsin the absence of extrinsic supervision -- all of which are hallmarks of humanlearning.

Possibility Before Utility: Learning And Using Hierarchical Affordances