  We propose DeepSim, a reinforcement learning environment build toolkit for
ROS and Gazebo. It allows machine learning or reinforcement learning
researchers to access the robotics domain and create complex and challenging
custom tasks in ROS and Gazebo simulation environments. This toolkit provides
building blocks of advanced features such as collision detection, behaviour
control, domain randomization, spawner, and many more. DeepSim is designed to
reduce the boundary between robotics and machine learning communities by
providing Python interface. In this paper, we discuss the components and design
decisions of DeepSim Toolkit.
