Reinforcement Learning

Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Reinforcement learning differs from supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience.

Fig.1. Reinforcement learning Algorithms and Applications (TechVidvan.com)

Applications of reinforcement learning were in the past limited by weak computer infrastructure. However, as Gerard Tesauro’s backgammon 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.

[Note: Get Machine learning Dissertation Topic and Full writing help]  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.

References

https://deepsense.ai/what-is-reinforcement-learning-the-complete-guide/