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Reinforcement Learning
                 Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an
                 environment  to  maximise  cumulative  rewards.  The  agent  learns  through  trial  and  error,  taking  actions,  and  receiving
                 feedback in the form of rewards or penalties. The objective is to develop a policy or strategy that enables the agent to
                 take actions that results in the highest cumulative reward over a period of time. Reinforcement learning is often applied
                 in situations requiring a series of decisions, such as playing games, controlling robots, or managing financial portfolios.
                 Examples of reinforcement learning algorithms include Q-learning, Deep Q-Networks (DQN), policy gradients, and
                 actor-critic methods.
                 This technique is also employed in real-world scenarios, like autonomous driving, where vehicles learn to navigate by
                 interacting with their surroundings. In healthcare, reinforcement learning optimises treatment plans by continuously
                 adjusting  based  on  patient  responses.  Additionally,  in  recommendation  systems,  it  enhances  user  experience  by
                 learning to suggest content, based on ongoing user interactions and feedback.



                                 Input Raw Data                   Environment                    Output

                                                          Reward           Best Action










                                                                          Selection of
                                                          State            Algorithm

                                                                     Agent




                        Benefits of AI


                 Some benefits of AI are as follows:
                    •  Increased efficiency and productivity: AI automates tasks, analyses data rapidly, and optimises processes, boosting
                   efficiency and productivity across various industries.
                    •  Improved decision-making: AI processes vast amounts of data and uncovers patterns that humans might miss,
                   aiding in data-driven decision-making and potentially leading to better outcomes.

                    •  Enhanced innovation and creativity: AI tools can generate new ideas, explore possibilities, and automate repetitive
                   tasks, allowing human resources to focus on more creative and innovative pursuits.
                    •  Progress in science and healthcare: AI supports drug discovery, medical diagnosis, and personalised medicine,
                   thereby driving advancements in healthcare and scientific research.
                    • Fraud detection: AI can detect fraudulent activities by analysing transaction patterns and identifying anomalies.


                        Limitations of AI


                 Some limitations of AI are as follows:
                    •  Job displacement: Automation through AI raises concerns about job loss and the necessity for workforce retraining
                   and upskilling.

                    •  Ethical  considerations: There  are  concerns about  bias in AI algorithms,  potential  misuse  for  surveillance or
                   manipulation, and the need for ethical guidelines and regulations.

                                                                  Introduction: Artificial Intelligence for Everyone   129
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