The Gym toolkit provides a standardized way of defining the interface for environments developed for problems that can be solved using reinforcement learning. If you are familiar with or have heard of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), you may realize how much of an impact a standard benchmarking platform can have on accelerating research and development. For those of you who are not familiar with ILSVRC, here is a brief summary: it is a competition where the participating teams evaluate the supervised learning algorithms they have developed for the given dataset and compete to achieve higher accuracy with several visual recognition tasks. This common platform, coupled with the success of deep neural network-based algorithms popularized by AlexNet (https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf), paved the way for the deep learning era we are in at the moment.
In a similar way, the Gym toolkit provides a common platform to benchmark reinforcement learning algorithms and encourages researchers and engineers to develop algorithms that can achieve higher rewards for several challenging tasks. In short, the Gym toolkit is to reinforcement learning what ILSVRC is to supervised learning.
