Reinforcement learning is a type of dynamic programming where the software learns from its environment to produce an output that will maximize the reward. Here the software requires no external agent but learns from the surrounding processes in the environment.

Some practical examples of reinforcement learning techniques are:

• Self driving cars: Self driving cars exhibit autonomous motion by learning from the environment. The robust vision technologies in such a system are able to adapt from surrounding traffic conditions. Thus, when these technologies are amalgamated with complex software and hardware movements, they make it possible to navigate through the traffic.

• Intelligent gaming programs: DeepMind's artificially intelligent G program has been successful in learning a number of games in a matter of hours. Such systems use reinforcement learning in the background to quickly adapt game moves. The G program was able to beat world known AI chess agent Stockfish with just four hours of training: