  Creating reinforcement learning (RL) agents that are capable of accepting and
leveraging task-specific knowledge from humans has been long identified as a
possible strategy for developing scalable approaches for solving long-horizon
problems. While previous works have looked at the possibility of using symbolic
models along with RL approaches, they tend to assume that the high-level action
models are executable at low level and the fluents can exclusively characterize
all desirable MDP states. Symbolic models of real world tasks are however often
incomplete. To this end, we introduce Approximate Symbolic-Model Guided
Reinforcement Learning, wherein we will formalize the relationship between the
symbolic model and the underlying MDP that will allow us to characterize the
incompleteness of the symbolic model. We will use these models to extract
high-level landmarks that will be used to decompose the task. At the low level,
we learn a set of diverse policies for each possible task subgoal identified by
the landmark, which are then stitched together. We evaluate our system by
testing on three different benchmark domains and show how even with incomplete
symbolic model information, our approach is able to discover the task structure
and efficiently guide the RL agent towards the goal.
