Applied Reinforcement Learning II: Implementation of Q-Learning

In order to make this article didactic, a simple and basic environment has been chosen that does not add too much complexity to the training, so that the learning of the Q-Learning algorithm can be fully appreciated. The environment is OpenAI Gym's Taxi-v3 [1], which consists of a grid world where the agent is a taxi driver who must pick up a customer and drop him off at his destination. As for the action space, the following discrete actions are available for the agent to interact with the environment: go forward, go backward, go right, go left, pick up a passenger and drop him off. This makes a total of 6 possible actions, which in turn are encoded in numbers from 0 to 5 for ease of programming. The correspondences between actions and numbers are shown in Figure 1.