Reinforcement learning (RL) is a powerful technique for learning optimal policies from trial and error. However, designing a reward function that captures the desired behavior of an agent is often a challenging and tedious task, especially when the agent has to deal with complex and multi-objective problems. To address this issue, researchers have proposed to use higher-level languages, such as Signal Temporal Logic (STL), to specify reward functions in a declarative and expressive way, and then automatically compile them into lower-level functions that can be used by standard RL algorithms. In this paper, we present RMLGym, a tool that integrates RML, a runtime verification tool, with OpenAI Gym, a popular framework for developing and comparing RL algorithms. RMLGym allows users to define reward functions using RML specifications and then generates reward monitors that evaluate the agent’s performance and provide feedback at each step. We demonstrate the usefulness and flexibility of RMLGym by applying it to a famous benchmark problem from OpenAI Gym, and we analyze the strengths and limitations of our approach.