title: iterations and reward in q-learning
body: <p>Good morning,
  In Q-learning, the agents take actions until reaching their goal. The algorithm is executed many times until obtaining convergence. For example, the goal is to obtain a maximum throughput until the end of the time simulation. The simulation time is divided into n equal periods T and the reward varies over time. So, the agents update their states n times at the begenning of each period. In this case, n is considered as the number of steps or iterations? In addition, the update of the Q-value is done after executing the selected action or before the execution (using the reward function which is an approximation of the real reward)?
  I would be grateful if you can answer to my questions. </p>