Reinforcement Learning (RL) has already achieved several breakthroughs on complex, high-dimensional, and even multi-agent tasks, gaining increasingly interest from not only the research
community. Although very powerful in principle, its applicability is still limited to solving games and control problems, leaving plenty opportunities to apply and develop RL algorithms for (but
not limited to) scientific domains like physics, and biology. Apart from the domain of interest, the applicability of RL is also limited by numerous difficulties encountered while training agents, like training instabilities and sensitivity to hyperparameters. For such reasons, we propose a modern,
modular, simple and understandable Python RL library called reinforce-lib. Our main aim is to enable newcomers, practitioners, and researchers to easily employ RL to solve new scientific problems. Our library is available at https://github.com/Luca96/reinforce-lib.
The paper is organized as follows: in section 1 we introduce and motivate our contribution, in sections 2 and 3 we provide a short introduction to the reinforcement learning paradigm,
as well as describing three popular families of algorithms, then, in the following section 4 we
present our library, finally section 5 concludes our discussion.