In this work, we propose the use of symbolic automata as formal specifications for reinforcement learning agents. The use of symbolic automata serves as a generalization of both bounded-time temporal logic-based specifications and deterministic finite automata, allowing us to describe input alphabets over metric spaces. Furthermore, our use of symbolic automata allows us to define non-sparse potential-based rewards which empirically shape the reward surface, leading to better convergence during RL. We also show that our potential-based rewarding strategy still allows us to obtain the policy that maximizes the satisfaction of the given specification.