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Reinforcement Learning

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. It receives feedback in the form of rewards or penalties based on its actions. The goal is to maximize the total reward over time by discovering which actions lead to the best outcomes. This approach is similar to how humans learn from experiences—trying different strategies, observing the results, and adjusting behavior to achieve desired results. RL is widely used in areas such as robotics, gaming, and autonomous systems, where optimal decision-making is crucial.

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  • Image for Reinforcement Learning

    Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions. Over time, it learns which actions lead to better outcomes and adjusts its behavior accordingly. This process is similar to how humans learn through trial and error. The goal is to maximize the total reward, allowing the agent to effectively navigate challenges and improve its performance in various tasks or scenarios.