Reinforcement Learning Algorithms with Python

OpenAI Gym and RL cycles

Since RL requires an agent and an environment to interact with each other, the first example that may spring to mind is the earth, the physical world we live in. Unfortunately, for now, it is actually used in only a few cases. With the current algorithms, the problems stem from the large number of interactions that an agent has to execute with the environment in order to learn good behaviors. It may require hundreds, thousands, or even millions of actions, requiring way too much time to be feasible. One solution is to use simulated environments to start the learning process and, only at the end, fine-tune it in the real world. This approach is way better than learning just from the world around it, but still requires ...