A simulation framework based on the open-source robotic software Gazebo and the Robot Operating System (ROS) is presented for articulated tracked robots, designed for reinforcement learning-based (RL) control skill acquisition. In particular, it is destined to serve as a research tool in the development and evaluation of methods in the domain of mobility learning for articulated tracked robots, in 3D indoor environments. Its architecture allows to interchange between different RL libraries and algorithm implementations, while learning can be customized to endow specific properties within a control skill. To demonstrate its utility, we focus on the most demanding case of staircase ascent and descent using depth image data, while respecting safety via reward function shaping and incremental, domain randomization-based, end-to-end learning.