  Model-based reinforcement learning algorithms are typically more sample
efficient than their model-free counterparts, especially in sparse reward
problems. Unfortunately, many interesting domains are too complex to specify
the complete models required by traditional model-based approaches. Learning a
model takes a large number of environment samples, and may not capture critical
information if the environment is hard to explore. If we could specify an
incomplete model and allow the agent to learn how best to use it, we could take
advantage of our partial understanding of many domains. Existing hybrid
planning and learning systems which address this problem often impose highly
restrictive assumptions on the sorts of models which can be used, limiting
their applicability to a wide range of domains. In this work we propose SAGE,
an algorithm combining learning and planning to exploit a previously unusable
class of incomplete models. This combines the strengths of symbolic planning
and neural learning approaches in a novel way that outperforms competing
methods on variations of taxi world and Minecraft.
