Simion Zoo: A training workbench for reinforcement learning allowing distributed experimentation

Simion Zoo is a Reinforcement Learning (RL) workbench developed for training of novel users that can be deployed over computer farms allowing extensive experimentation over distributed resources. In this paper, we present this software platform and share some of the insights gained during the development. This workbench provides a complete set of tools to design, run, carry out statistical analysis of the results, report preparation, and have qualitative visual assessment of the simulation evolution of continuous RL control experiments. The main features that set apart Simion Zoo from other software packages for introduction to RL experimentation are its easy-to-use GUI, its support for distributed execution including deployment over graphics processing units (GPUs), and the possibility to explore concurrently the RL hyper-parameter space, which is key to successful RL experimentation.