Q-Learning Algorithm: From Explanation to Implementation

Well, let's recall some definitions and equations that we need for implementing the Q-Learning algorithm. In RL, we have an environment that we want to learn. For doing that, we build an agent who will interact with the environment through a trial-error process. At each time step t, the agent is at a certain state s_t and chooses an action a_t to perform. The environment runs the selected action and returns a reward to the agent.