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Finally, after multiple attempts and adjustments, the machine
              predicts the object correctly as a ball. Positive feedback is given,
              and the machine has successfully learned to identify the ball.            Reinforcement Model      Ball
              Other  examples  of  Reinforcement  Learning  are  self-driving
              cars, robotics and a variety of video games available these days.
              1.  Video  Game  AI:  A  video  game  character  (AI)  learns  to
                  play a game like chess, Go, or Atari games. In video game
                  AI, Reinforcement Learning allows agents (e.g., characters or bots) to learn optimal strategies by exploring
                  the game environment, receiving rewards for achieving objectives, and penalties for failing, improving their
                  performance over time.
              2.  Car Parking: In car parking, Reinforcement Learning helps the car learn how to park properly by trying different
                  ways, getting rewards when it parks correctly, and getting penalties if it makes a mistake like hitting something.
              3.  Humanoid Walking: Humanoid walking is related to Reinforcement Learning as the robot learns to walk by
                  receiving rewards for stable movements and penalties for falls, adjusting its actions to maximise balance and
                  efficiency over time.
              What Makes it Different?


              Reinforcement Learning  is  unique because of its ability to handle situations where  traditional  methods like
              supervised or unsupervised learning may fall short. In these approaches, you typically need a clear understanding
              of the data and the problem you're solving. However, real-world scenarios often involve complex and dynamic
              environments that are not fully understood, and data might be lacking in certain situations. Additionally, the
              environment may change over time, requiring the system to adapt. Reinforcement Learning stands out because it
              doesn't rely on extensive pre-existing knowledge or large datasets, allowing it to learn from interactions with the
              environment and adapt to unforeseen circumstances, making it highly effective in dynamic & uncertain settings.

              Difference between Supervised Learning, Unsupervised Learning and
              Reinforcement Learning


               Aspect      Supervised Learning     Unsupervised Learning      Reinforcement Learning

               Nature of  Labelled data with       Unlabelled data; no        No labelled data; the model learns through
               Data        input-output pairs.     predefined output labels.  interaction  with  the  environment  and
                                                                              receives rewards or penalties for actions.

               Learning    Learns a direct         Learns patterns, clusters, or  Learns to take actions that maximise
               Process     mapping between input  structure in data without   long-term rewards based on trial and error.
                           and output.             predefined labels.
               Feedback    Instant feedback based   No explicit feedback, learns  Feedback is delayed and occurs based on
                           on the correct label    patterns or relationships.  the outcomes of actions.

              Sub-categories of Supervised Learning Models

              The Supervised Learning is further categorised as: Classification and Regression. Let us learn about these in detail.

              Classification


              Classification  is  a  rule-based  AI  model.  It  is  a  systematic  grouping  of  observations  in  classes,  something  like
              categorising plants, animals in different categories by biologists. In classification you teach the machine to perform
              with labelled data. Testing data is then classified as one of the labels of the training dataset.

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