Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks

In recent years, Reinforcement Learning (RL) has become a very popular area of research, because of the almost exponential increase in computing power due to the advent of dedicated GPUs that have empowered researchers to face previously unaffordable problems. In particular, the successful applications of Deep Reinforcement Learning (DRL)to produce master videogame players [10, 7] have created great expectations about the potential of DRL, even outside the academic research community. As a result of this popularity boost, the number of RL software packages has grown significantly. Nevertheless, these projects are mostly oriented towards the research community, i.e. they assume sophisticated programming users with powerful computing resources to run the software. Even for sophisticated programmers, these packages impose a steep learning curve that hinders their user experience. This is in stark contrast with the de-facto user standards forSupervised Learning (SL) software, which customarily allow users to design/run experiments, and to analyze the results on an intuitive Graphical User Interface (GUI) that allows a swift learning curve. Users without programming skills that intend to design and run RL experiments quickly on inexpensive and commonly available hardware will obviously appreciate such kind of facilities.