{"id":8839,"date":"2019-10-29T23:57:57","date_gmt":"2019-10-29T18:27:57","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/reinforcement-machine-learning\/"},"modified":"2024-09-12T16:01:34","modified_gmt":"2024-09-12T10:31:34","slug":"reinforcement-machine-learning","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/reinforcement-machine-learning\/","title":{"rendered":"Reinforcement Learning"},"content":{"rendered":"\n

You 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. <\/span>
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<\/a> 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. <\/span>
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 comprehensive Tutorial on Reinforcement learning along with a case study.<\/span><\/p>\n\n\n\n

\n