Data scientists looking to push the ball forward in the field of reinforcement learning may want to check out RLlib, a new library released as open source last month by researchers affiliated with RISELab. According to researchers, the goal of RLlib is to enable users to break down the various components that go into a reinforcement learning, thereby making them more scalable, easier to integrate, and easier to resuse. Reinforcement learning is a type of supervised learning that's gaining popularity as a way to quickly train programs to perform tasks optimally in a world awash in less-than-optimal training data. Instead of training a model with pristine data, which is ideal in supervised learning, the reinforcement learning model learns from the data environment as it naturally exists, and uses a simple feedback mechanism (the reinforcement signal) to nudge the model towards the ideal solution. The practical advantage of the reinforcement approach is that it seeks to achieve a balance between being able to interpret uncharted data (which is where unsupervised learning algorithms flourish) and exploiting existing knowledge (where supervised learning typically excels).