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In reinforcement learning, AI agents learn through trial and error, receiving feedback from their actions, which helps them improve over time
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The core of reinforcement learning lies in the concept of rewards and penalties, where agents aim to maximize their cumulative rewards by making optimal decisions
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Reinforcement learning finds applications in robotics, game playing, recommendation systems, and autonomous vehicles, among others
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AI agents must strike a balance between exploring new actions and exploiting known ones, a critical challenge in reinforcement learning
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Deep reinforcement learning combines reinforcement learning with neural networks, enabling more complex tasks and higher performance
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Discuss the challenges such as scalability and ethical considerations while looking at the bright prospects of reinforcement learning in AI
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Reinforcement learning in AI offers a dynamic framework for machines to learn, adapt, and excel in a wide array of applications, promising transformative advancements in the field
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