Reinforcement Machine Learning

Reading Time: 6 minutesYou might have seen robots doing mundane tasks like cleaning room or serving beer to people. However, these actions are usually remote-controlled by a human. These robots are physically capable of doing things following a set of instructions given to them, but they lack the basic intelligence to decide and do things by themselves. Embedding intelligence is a software challenge, and reinforcement learning, a subfield in machine learning, provides a promising direction towards developing intelligent robotics. 

Reinforcement learning is concerned with how an agent uses the feedback to evaluate its actions and plan about future actions in the given environment to maximize the results. In reinforcement learning, the agent is empowered to decide how to perform a task, which makes it different from other such machine learning models where the agent blindly follows a set of instructions given to it. The machine acts on its own, not according to a set of pre-written commands. Thus, reinforcement learning denotes those algorithms, which work based on the feedback of their actions and decide how to accomplish a complex task. 

These algorithms are rewarded when they make the right decision and are punished when they make the wrong decision. Under favourable conditions, they can do a superhuman performance. Here is an introduction to reinforcement machine learning and its applications. 


Importance of Reinforce Learning

We need technological assistance to simplify life, improve productivity and to make better business decisions. To achieve this goal, we need intelligent machines. While it is easy to write programs for simple tasks, we need a way out to build machines that carry out complex tasks. To Achieve this is to create machines that are capable of learning things by themselves. Reinforce learning does this.

Originally Posted at: Reinforcement Machine Learning by administrator

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