Deep Q-Learning with Python and TensorFlow 2.0

In the previous two articles we started exploring the interesting universe of reinforcement learning. First we went through the basics of third paradigm within machine learning – reinforcement learning. Just to freshen up our memory, we saw that approach of this type of learning is unlike the previously explored supervised and unsupervised learning. In reinforcement learning, self-learning agent learns some type of interaction between it and the environment. The agent wants to achieve some kind of goal within mentioned environment while it interacts with it. This interaction is divided into time steps.