Reinforcement Learning is an aspect of Machine learning where an agent learns to behave in an environment, by performing certain actions and observing the rewards/results which it get from those actions.

With the advancements in Robotics Arm Manipulation, Google Deep Mind beating a professional Alpha Go Player, and recently the OpenAI team beating a professional DOTA player, the field of reinforcement learning has really exploded in recent years.

Applications of reinforcement learning were in the past limited by weak computer infrastructure. However, as Gerard Tesauro’s backgamon AI superplayer developed in 1990’s shows, progress did happen. That early progress is now rapidly changing with powerful new computational technologies opening the way to completely new inspiring applications.

Training the models that control autonomous cars is an excellent example of a potential application of reinforcement learning. In an ideal situation, the computer should get no instructions on driving the car. The programmer would avoid hard-wiring anything connected with the task and allow the machine to learn from its own errors. In a perfect situation, the only hard-wired element would be the reward function

Uses

1. RL is quite widely used in building AI for playing computer games. AlphaGo Zero is the first computer program to defeat a world champion in the ancient Chinese game of Go. Others include ATARI games, Backgammon ,etc

2. In robotics and industrial automation, RL is used to enable the robot to create an efficient adaptive control system for itself which learns from its own experience and behavior. DeepMind’s work on Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Policy updates is a good example of the same. Watch this interesting demonstration video.