Nowadays, Deep Reinforcement Learning (RL) is one of the hottest topics in the Data Science community. The fast development of RL has resulted in the growing demand for easy to understand and convenient to use RL tools. That’s why it is important to pick a library that will be quick, reliable, and relevant for your RL task.
In this post, I’ll share with you my library of environments that support training reinforcement learning (RL) agents. The basis for RL research, or even playing with or learning RL, is the environment. It’s where you run your algorithm to evaluate how good it is. We’re going to explore 23 different benchmarks, so I guarantee you’ll find something interesting!