Deep Learning with TensorFlow

Reinforcement Learning

Reinforcement Learning is based on an interesting psychological theory:

Applying a reward immediately after the occurrence of a response increases its probability of reoccurring, while providing punishment after the response will decrease the probability (Thorndike, 1911).

A reward, received immediately after the execution of a correct behavior, increases the likelihood that this behavior will be repeated; while, following an undesired behavior, the application of a punishment decreases the likelihood of that error reocurring. Therefore, once a goal has been established, Reinforcement Learning seeks to maximize the rewards received, to achieve the designated goal.

RL finds applications in different contexts in which ...