Page 161 - AI Ver 1.0 Class 10
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                 Reinforcement Learning
                 Reinforcement learning is a type of learning based approach where a machine learning algorithm enables an agent
                 (machine with an intelligent code) to learn in an environment to find the best possible behavior or path it should
                 take by performing certain actions that maximize the total cumulative reward of the agent.
                 In  this  learning  approach  the  agent  learns  automatically  by  using  hit  and  trial  methods  or  through  its  own
                 experience using rewards and penalties. Each action performed by an agent gives reward for correct move and
                 it signals positive feedback. For wrong move it generates negative feedback and gets punishment and a penalty.

                 The agent explores the environment by interacting with it freely so that it is able to improve the performance by
                 getting the maximum positive rewards.
                 The best applications of reinforcement learning are self driving cars, robotics and a variety of video games available
                 these days.


                 Stage 5: Evaluation
                 This is a very important stage of AI Project designing and training where we properly test the system to find out
                 the efficiency and performance of the model.







                                                       Business              Data
                                                    Understanding        Understanding




                                                                                    Data
                                                                                 Preparation
                                            Deployment


                                                                                  Modeling


                                                                 Data


                                                                Evaluation






                 After the model is designed and trained then the reliability of the model is checked using Testing Data acquired at
                 the Data Acquisition stage.

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