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You may wonder why we need to test reinforcement learning algorithms on Gym's environments since the actual environments we work in can be a lot different. You will recall that reinforcement learning doesn't make many assumptions about the environment, but it gets to know more about the environment by interacting with it. Also, when comparing the performance of different algorithms, we need to apply them to standardized environments. Gym is a perfect benchmark, covering many versatile and easy-to-use environments. This is similar to the datasets that we often use as benchmarks in supervised and unsupervised learning, such as MNIST, Imagenet, MovieLens, and Thomson Reuters News.