There has been increasing interest in using symbolic models along with reinforcement learning (RL) problems, where these coarser abstract models are used as a way to provide higher level guidance to the RL agent. However, most of these works are limited by their assumption that they have access to a symbolic approximation of the underlying problem. To address this problem, we introduce a new method for learning optimistic symbolic approximations of the underlying world model. We will see how these representations, coupled with fast diverse planners developed from the automated planning community, provides us with a new paradigm for optimistic exploration in sparse reward settings. We also investigate how we could speed up the learning process by generalizing learned model dynamics across similar actions with minimal human input. We will evaluate the method, by testing it on multiple benchmark domains and compare it with other RL strategies for sparse reward settings, including hierarchical RL and intrinsic reward based exploration.