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Reinforcement learning serves as a fundamental building block for AI, enabling machines to learn from interactions and improve performance
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Reinforcement learning relies on a reward system to guide the AI agent towards desired outcomes and reinforce positive behaviors
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Reinforcement learning balances exploration (trying new actions) and exploitation (leveraging existing knowledge) to optimize decision-making
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AI agents using reinforcement learning can continuously learn and adapt to new environments, ensuring adaptability in dynamic scenarios
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Reinforcement learning finds applications in various domains, including robotics, gaming, finance, healthcare, and autonomous vehicles
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