This tutorial describes a simulated laboratory for making use of reinforcement learning (RL) for chemical discovery. A key advantage of the simulated environment is that it enables RL agents to be trained safely and efficiently. In addition, it offer an excellent testbed for RL in general, with challenges which are uncommon in existing RL benchmarks. The simulated laboratory, denoted ChemGymRL, is open-soure, implemented according to the standard Gymnasium API, and is highly customizable. It supports a series of interconnected virtual chemical \emph{benches} where RL agents can operate and train. Within this tutorial introduce the environment, demonstrate how to train off-the-shelf RL algorithms on the benches, and how to modify the benches by adding additional reactions and other capabilities. In addition, we discuss future directions for ChemGymRL benches and RL for laboratory automation and the discovery of novel synthesis pathways. The software, documentation and tutorials are available here: \url{ur_ suppressed}.