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强化学习 让我以另一种方式玩游戏吧
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强化学习是一种机器学习的类型，涉及代理通过反复试验来学习如何在环境中做出决策。代理的目标是最大化由环境给出的奖励信号。代理学习采取导致最大可能奖励的行动，同时避免导致负面结果的行动。
Richard S. Sutton和Andrew G. Barto的《强化学习导论》一书全面介绍了强化学习领域。该书涵盖价值函数、蒙特卡罗方法、时序差分学习和策略梯度等主题。
该书的第一版于1998年出版，第二版目前正在编写中。第二版根据领域内最新进展更新了材料，并增加了有关深度强化学习和多智能体强化学习的新章节。
该书被广泛认为是关于强化学习的最权威的文本之一，并被该领域的研究人员和实践者用作参考。它适合本科和研究生学生，并为任何对学习或从事强化学习感兴趣的人提供了坚实的基础。
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AirSim是由微软开发的一个开源的模拟器，用于模拟无人机、汽车和机器人等各种类型的机器人的行为和环境。它提供了高度可定制的环境，允许用户在虚拟场景中测试各种机器人算法，包括视觉SLAM、路径规划、控制等等。
AirSim的最大特点是其高度逼真的图形渲染引擎和物理模拟引擎。它使用了虚幻引擎作为渲染引擎，并使用了现代计算机图形学技术来模拟各种物理现象，例如惯性、空气阻力、摩擦力等等，以使得机器人在仿真环境中的行为和现实世界中的行为尽量相似。
AirSim还提供了一套API，使得用户可以轻松地控制和监测机器人的状态。这些API可以用C++、Python和ROS等语言和框架进行访问。
总之，AirSim为机器人研究和开发人员提供了一个快速、高效、低成本的测试平台，可以加速机器人技术的发展。
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