Reinforcement learning (RL) is a subset of machine learning that allows intelligent agents to acquire the ability of executing desired actions through interactions with an environment. Its remarkable progress has achieved significant results in diverse domains, such as Go and StarCraft, and practical challenges like protein-folding. This short paper presents overviews of two common RL approaches: the Monte Carlo and temporal difference methods. To obtain a more comprehensive understanding of these concepts and gain practical experience, readers can access the full article on IEEE Xplore, which includes interactive materials and examples.

中文翻译：

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强化学习中的蒙特卡洛和时间差分方法 [AI-eXplained]


强化学习 (RL) 是机器学习的一个子集，它允许智能代理通过与环境交互来获得执行所需操作的能力。它的显着进步在围棋和星际争霸等不同领域以及蛋白质折叠等实际挑战中取得了重大成果。这篇简短的论文概述了两种常见的强化学习方法：蒙特卡罗方法和时间差分方法。为了更全面地了解这些概念并获得实践经验，读者可以访问 IEEE Xplore 上的完整文章，其中包括交互式材料和示例。