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<title>kizzy的个人博客 - 如何使用强化学习玩游戏</title>
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<h1>如何使用强化学习玩游戏</h1>
Fri 29 May 2020
by <a class="url fn" href="https://kuikui.tech/author/kizzy.html">kizzy</a>
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<div><p> 随着机器学习的热潮重新回归，越来越多受困于算力的项目被重启，强化学习就是其中之一。那么什么是强化学习呢？</p>
<blockquote>
<p>强化学习（Reinforcement Learning, RL），又称再励学习、评价学习或增强学习，是<a href="https://baike.baidu.com/item/机器学习/217599">机器学习</a>的范式和<a href="https://baike.baidu.com/item/方法论/82748">方法论</a>之一，用于描述和解决<a href="https://baike.baidu.com/item/智能体/9446647">智能体</a>（agent）在与环境的交互过程中通过学习策略以达成回报最大化或实现特定目标的问题。</p>
<p>强化学习理论受到<a href="https://baike.baidu.com/item/行为主义心理学/1331310">行为主义心理学</a>启发，侧重在线学习并试图在探索-利用（exploration-exploitation）间保持平衡。不同于<a href="https://baike.baidu.com/item/监督学习/9820109">监督学习</a>和<a href="https://baike.baidu.com/item/非监督学习/16588789">非监督学习</a>，强化学习不要求预先给定任何数据，而是通过接收环境对动作的奖励（反馈）获得学习信息并更新模型参数 。</p>
<p>强化学习问题在<a href="https://baike.baidu.com/item/信息论/302185">信息论</a>、<a href="https://baike.baidu.com/item/博弈论/81545">博弈论</a>、<a href="https://baike.baidu.com/item/自动控制/5337539">自动控制</a>等领域有得到讨论，被用于解释<a href="https://baike.baidu.com/item/有限理性/1125108">有限理性</a>条件下的平衡态、设计<a href="https://baike.baidu.com/item/推荐系统/10267357">推荐系统</a>和机器人交互系统 [3] 。一些复杂的强化学习算法在一定程度上具备解决复杂问题的通用智能，可以在围棋和电子游戏中达到人类水平 。</p>
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<p>好像</p></div>
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