Flexible Option Learning
This work addresses a bottleneck in hierarchical RL for researchers and practitioners by enabling more efficient temporal abstraction, though it is incremental as it builds on existing methods.
The paper tackles the problem of inefficient knowledge transfer in hierarchical reinforcement learning by revisiting and extending intra-option learning to update all options simultaneously, resulting in significant improvements in performance and data-efficiency across various domains.
Temporal abstraction in reinforcement learning (RL), offers the promise of improving generalization and knowledge transfer in complex environments, by propagating information more efficiently over time. Although option learning was initially formulated in a way that allows updating many options simultaneously, using off-policy, intra-option learning (Sutton, Precup & Singh, 1999), many of the recent hierarchical reinforcement learning approaches only update a single option at a time: the option currently executing. We revisit and extend intra-option learning in the context of deep reinforcement learning, in order to enable updating all options consistent with current primitive action choices, without introducing any additional estimates. Our method can therefore be naturally adopted in most hierarchical RL frameworks. When we combine our approach with the option-critic algorithm for option discovery, we obtain significant improvements in performance and data-efficiency across a wide variety of domains.