Reinforcement Learning Algorithms with Python

From zero to one

Once you have defined the algorithm that best fits your needs, whether that's one of the well-known algorithms or a new one, you have to develop it. As you saw throughout this book, reinforcement learning algorithms don't have much in common with supervised learning algorithms. For this reason, there are different aspects that are worth pointing out in order to facilitate the debugging, experimentation, and tuning of the algorithm:

• Start with easy problems: Initially, you would want to experiment with a workable version of the code as fast as possible. However, it would be advisable to gradually proceed with increasingly complex environments. This will greatly help to reduce the overall training and debugging time. Let me ...