Reinforcement learning can train policies that effectively perform complex tasks. However, the performance of these methods degrades as the horizon increases, and performing long-horizon tasks often requires reasoning over and composing multiple lower-level skills. Hierarchical reinforcement learning aims to enable this, by providing a bank of low-level skills as action abstractions, in the form of primitives or options. However, an effective hierarchy should exhibit abstraction both in the space of actions and states. We posit that a suitable state abstraction for the higher-level policy should depend on the capabilities of the available lower-level policies, and we propose an approach that produces such a representation by using the value functions corresponding to each lower-level skill to capture the affordances for these skills. Empirical evaluations for maze-solving and robotic manipulation tasks demonstrate that our approach improves long-horizon performance and enables better zero-shot generalization than popular model-free and model-based methods by constructing a compact state abstraction that represents the affordances of the scene and is robust to distractors.
We implement our approach in two domains: a long-horizon maze solving task, and a complex image-based robotic manipulation simulator. In both settings, we show empirically that, when provided with a suitable bank of skills, our approach enables more effective long-horizon control as compared to alternative state representation learning methods.