Hands-On Q-Learning with Python

Implementing your agent

Let's recreate the Taxi-v2 environment. We'll need to import numpy this time. We'll be using the term state instead of observation in this chapter for consistency with the terminology we used in Chapter 1, Brushing Up on Reinforcement Learning Concepts:

import gymimport numpy as npenv = gym.make('Taxi-v2')state = env.reset()

Create the Q-table as follows:

Q = np.zeros([env.observation_space.n, env.action_space.n])

The Q-table is initialized as a two-dimensional numpy array of zeroes. The first three rows of the Q-table currently look like this:

State	South(0)	North(1)	East(2)	West(3)	Pickup(4)	Dropoff(5)
0	0	0	0	0	0	0
1	0	0	0	0	0	0
2	0	0	0	0	0	0

The first column represents the state, and the other column ...