  Long-term planning poses a major difficulty to many reinforcement learning
algorithms. This problem becomes even more pronounced in dynamic visual
environments. In this work we propose Hierarchical Planning and Reinforcement
Learning (HIP-RL), a method for merging the benefits and capabilities of
Symbolic Planning with the learning abilities of Deep Reinforcement Learning.
We apply HIPRL to the complex visual tasks of interactive question answering
and visual semantic planning and achieve state-of-the-art results on three
challenging datasets all while taking fewer steps at test time and training in
fewer iterations. Sample results can be found at youtu.be/0TtWJ_0mPfI
