📌 Reinforcement Learning Summary
Reinforcement Learning is a type of machine learning where an agent learns to make decisions by interacting with its environment. The agent receives feedback in the form of rewards or penalties and uses this information to figure out which actions lead to the best outcomes over time. The goal is for the agent to learn a strategy that maximises its total reward through trial and error.
🙋🏻♂️ Explain Reinforcement Learning Simply
Imagine teaching a dog tricks by giving it treats when it does something right and ignoring it when it gets it wrong. Over time, the dog learns which actions earn rewards. In Reinforcement Learning, computers learn in a similar way, getting better at tasks by practising and receiving feedback from their environment.
📅 How Can it be used?
Reinforcement Learning could be used to develop a self-learning robot that navigates a warehouse efficiently.
🗺️ Real World Examples
In online advertising, reinforcement learning can decide which adverts to show users by learning which choices lead to the most clicks or sales. The system tries different strategies and adapts its decisions to maximise engagement and profit over time.
In video games, reinforcement learning has been used to train AI agents that can play games like chess or Go at a superhuman level. These agents learn by playing millions of games against themselves, gradually improving their strategies with each outcome.
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