What is reinforcement learning in AI?
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Generative AI has a significant impact on creativity—both as a powerful enabler and a source of new challenges. Here's how it influences creativity across various dimensions:
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment to achieve a goal. Unlike supervised learning (which learns from labeled data) or unsupervised learning (which finds patterns in data), RL learns through trial and error, using feedback from its own actions.
How Reinforcement Learning Works:
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The agent takes an action in a given state of the environment.
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The environment responds by moving to a new state and gives the agent a reward (a numerical value).
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The agent’s goal is to maximize cumulative rewards over time by learning which actions lead to the best outcomes.
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Over many interactions, the agent develops a policy—a strategy mapping states to actions.
Key Concepts:
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Agent: The learner or decision-maker.
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Environment: The world the agent interacts with.
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State: A representation of the current situation.
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Action: A choice made by the agent.
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Reward: Feedback signal guiding learning.
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Policy: The strategy the agent uses to decide actions.
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Value function: Estimates how good it is to be in a given state, considering future rewards.
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