Reinforcement Learning Frameworks
In the previous sections, we learned the basic theory behind RL. In principle, an agent or an environment can be implemented in any way or any language. For RL, the primary language used by both academic and industrial people is Python, as it allows you to focus on the algorithms and not on the language details, making it very simple to use. Implementing, from scratch, an algorithm or a complex environment (that is, an autonomous driving environment) might be very difficult and error-prone. For this reason, several well-established and well-tested libraries make RL very easy for newcomers. In this section, we will explore the main Python RL libraries. We will present OpenAI Gym, a set of environments that is ready to use and easy to modify, and OpenAI Baselines, a set of high quality, state-of-the-art algorithms. By the end of this chapter, you will have learned about and practiced with environments and agents.
OpenAI Gym
OpenAI Gym (https:/...