Machine Learning is a field that has crazy advancements in the past couple of years. This trend and advancements have created a lot of Job opportunities in the industry. The need for Machine Learning Engineers are high in demand due to evolving technology and generation of huge amounts of data like Big Data. Reinforcement learning is an area of Machine Learning, It's about taking suitable action to maximize reward in a particular situation and is to be employed by various software and machines to find the best possible behavior that it should take in a specific situation. Reinforcement learning differs from the supervised learning in a way.
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 training data set it bounds to learn from its experience. There are two important learning models in reinforcement learning :
(a) Markov Decision Process. (b) Q-learning
Reinforcement learning solves a different kind of problem. In reinforcement learning, there is an agent that interacts with a certain environment, thus changing its state and receives rewards (or) penalties for its input. It's goal is to find patterns of actions by trying them all and comparing the results that yield the most reward points.
One of the key features of reinforcement learning is that the agents actions may not affect the immediate state of the environment but it impacts the subsequent ones.
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