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    Reinforcement Learning

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    yixeje1721
    Reinforcement learning is a type of machine learning where an agent learns how to achieve goals in an environment by receiving rewards or punishments for actions, allowing it to determine optimal actions over time. The agent interacts with the environment, receives feedback on its actions in the form of rewards, and uses that feedback to learn which actions yield the most reward. The goal is to maximize the total reward received by learning through trial and error which actions are best in each situation.

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    Reinforcement Learning

    Uploaded by

    yixeje1721
    13 views1 page
    Reinforcement learning is a type of machine learning where an agent learns how to achieve goals in an environment by receiving rewards or punishments for actions, allowing it to determine optimal actions over time. The agent interacts with the environment, receives feedback on its actions in the form of rewards, and uses that feedback to learn which actions yield the most reward. The goal is to maximize the total reward received by learning through trial and error which actions are best in each situation.

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    Reinforcement learning

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    Reinforcement learning is a type of machine learning where an agent learns how to achieve goals in an environment by receiving rewards or punishments for actions, allowing it to determine optimal actions over time. The agent interacts with the environment, receives feedback on its actions in the form of rewards, and uses that feedback to learn which actions yield the most reward. The goal is to maximize the total reward received by learning through trial and error which actions are best in each situation.

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    13 views1 page

    Reinforcement Learning

    Uploaded by

    yixeje1721
    Reinforcement learning is a type of machine learning where an agent learns how to achieve goals in an environment by receiving rewards or punishments for actions, allowing it to determine optimal actions over time. The agent interacts with the environment, receives feedback on its actions in the form of rewards, and uses that feedback to learn which actions yield the most reward. The goal is to maximize the total reward received by learning through trial and error which actions are best in each situation.

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    of 1

    Reinforcement Learning

    Reinforcement learning is a type of machine learning, in which an agent explores an environment to


    learn how to perform desired tasks by taking actions with good outcomes and avoiding actions with
    bad outcomes.

    A reinforcement learning model will learn from its experience and over time will be able to identify
    which action leads to the best rewards.

    In reinforcement learning, an agent interacts with an environment with an objective to maximize its
    total award.

    The agent takes an action based on the environment state and the environment returns the reward
    and next state. The agent learns from trial and error, initially taken random action and over time
    identifying the actions that lead to long-term rewards.

    Every reinforcement learning system consists of these four main components:

     An agent
     An interactive environment
     An algorithm that steers the action taken by the agent
     A feedback mechanism to reward/penalize the agent as per the action

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