Deep Learning with TensorFlow - Second Edition

The Q-Learning algorithm

Solving an RL problem requires an estimate, during the learning process, of an evaluation function. This function must be able to assess, through the sum of the rewards, the success of a policy.

The basic idea of Q-Learning is that the algorithm learns the optimal evaluation function for the entire space of states and actions (S × A). This so-called Q-function provides a match in the form Q: S × A -> R, where R is the expected value of the future rewards of an action executed in the state, . Once the agent has learned the ...