Hands-On Q-Learning with Python

Solving MDPs with RL

RL algorithms are designed to solve exactly the type of optimization problem an MDP frames; that is, to find an optimal decision-making policy to maximize the rewards offered by making decisions within this environment.

The rewards offered for taking each action are shown in the preceding MDP diagram as yellow arrows. When we take action a₀ and end up in state S₀, we get a reward of +5; and when we take action a₁ and end up in state S₀, we get a reward of -1.

The Taxi-v2 environment has 500 states, as we'll see shortly, so it is not practical to represent them all in a diagram such as the previous one. Instead, we will be enumerating them in our Q-table in the next section. We'll use a state vector to represent the variables ...