  We introduce a novel virtual robotic toolkit myGym, developed for
reinforcement learning (RL), intrinsic motivation and imitation learning tasks
trained in a 3D simulator. The trained tasks can then be easily transferred to
real-world robotic scenarios. The modular structure of the simulator enables
users to train and validate their algorithms on a large number of scenarios
with various robots, environments and tasks. Compared to existing toolkits
(e.g. OpenAI Gym, Roboschool) which are suitable for classical RL, myGym is
also prepared for visuomotor (combining vision & movement) unsupervised tasks
that require intrinsic motivation, i.e. the robots are able to generate their
own goals. There are also collaborative scenarios intended for human-robot
interaction. The toolkit provides pretrained visual modules for visuomotor
tasks allowing rapid prototyping, and, moreover, users can customize the visual
submodules and retrain with their own set of objects. In practice, the user
selects the desired environment, robot, objects, task and type of reward as
simulation parameters, and the training, visualization and testing themselves
are handled automatically. The user can thus fully focus on development of the
neural network architecture while controlling the behaviour of the environment
using predefined parameters.
