Lower limb exoskeletons show great potential for rehabilitation of patients with different motor impairments. However, robot neurorehabilitation is still far from being widely used in clinical practice given human-robot interaction limitations. Therefore, the rise of Reinforcement Learning offers an opportunity to tackle the complex problem of human-robot interaction, often avoided by classical position control with a trajectory reference. However, the challenge of transferring the agent from the native programming environment where it has been trained to the real world for inference presents several limitations. This paper proposes a method for externalizing the training environment through ROS, to face the limitations and challenges of an uncoupled environment since the beginning. To prove this concept, a hyper-realist simulator of the Exo-H3 exoskeleton is controlled by the Reinforcement Learning agent. All experiments were conducted using the Exo-H3 Gazebo simulator connected via ROS. Real hardware validation remains future work.