Reinforcement Learning Tutorial Part 3: Basic Deep Q-Learning

In part 1 we introduced Q-learning as a concept with a pen and paper example. In part 2 we implemented the example in code and demonstrated how to execute it in the cloud. In this third part, we will move our Q-learning approach from a Q-table to a deep neural net. With Q-table, your memory requirement is an array of states x actions. For the state-space of 5 and action-space of 2, the total memory consumption is 2 x 5 10.