While neural networks are responsible for recent breakthroughs in problems like computer vision, machine translation and time series prediction, they can also c

While neural networks are responsible for recent breakthroughs in problems like computer vision, machine translation and time series prediction, they can also combine with reinforcement learning algorithms. Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. They can start from a blank slate, and under the right conditions they achieve superhuman performance. Like a child reinforced by its mother, these algorithms are penalized when they make the wrong decisions and rewarded when they make the right ones – this is reinforcement. While that may sound trivial, it's a vast improvement over their previous accomplishments, and the state of the art is progressing rapidly. Reinforcement learning solves the difficult problem of correlating immediate actions with the delayed returns they produce. Like humans, reinforcement learning algorithms sometimes have to wait a while to see the fruit of their decisions. They operate in a delayed return environment, where it can be difficult to understand which action leads to which outcome over many time steps. Reinforcement learning algorithms can be expected to perform better and better in more ambiguous, real-life environments while choosing from an arbitrary number of possible actions, rather than from the limited options of a video game. That is, with time we expect them to be valuable to achieve goals in the real world.