Hands-On Artificial Intelligence for IoT

Taxi drop-off using Q-tables

The simple Q-learning algorithm involves maintaining a table of the size m×n, where m is the total number of states and n the total number of possible actions. Therefore, we choose a problem from the toy-text group since their state space and action space is small. For illustrative purposes, we choose the Taxi-v2 environment. The goal of our agent is to choose the passenger at one location and drop them off at another. The agent receives +20 points for a successful drop-off and loses 1 point for every time step it takes. There's also a 10-point penalty for illegal pick-up and drop-off. The state space has walls shown by | and four location marks, R, G, Y, and B respectively. The taxi is shown by box: the pick-up ...