Building a Q-learning agent

This recipe will show you how to build a Q-learning agent. Q-learning can be applied to model-free RL problems. It supports off-policy learning and therefore provides a practical solution to problems where available experiences were/are collected using some other policy or by some other agent (even humans).

Upon completing this recipe, you will have a working RL agent that, when acting in the GridworldV2 environment, will generate the following state-action value function using the SARSA algorithm:

Figure 2.18 – State-action values obtained using the Q-learning algorithm

Getting ready

To complete this recipe, you will need to activate the tf2rl-cookbook Python/conda virtual environment and run pip install -r requirements.txt. If the following import statements run without issues, you are ready to get started:

import numpy as np
import random

Now, let's begin.

How to do it…

Let's implement...