基于强化学习的机载风能优化
Optimizing Airborne Wind Energy with Reinforcement Learning
摘要：
空中风能是一种轻型技术，允许使用风筝和滑翔机等空中设备从风中提取电力，其中可以动态控制机翼方向，以最大限度地提高性能。湍流空气动力学的动力学复杂性使得该优化问题无法通过常规方法（例如经典控制理论）来解决，所述常规方法依赖于手头的动力学系统的精确且易处理的分析模型。在这里，我们建议通过强化学习来解决这个问题，强化学习是一种技术，通过与环境的反复试错交互，学习将观察与有利可图的行为相关联，而不需要系统的先验知识。我们证明，在模拟环境中，强化学习找到了一种有效的方法来控制风筝，使其能够长距离牵引车辆。我们使用的算法是基于一个小的直观的观察和它的物理透明的解释允许描述的近似最优策略作为一个简单的列表的操纵指令。
英文摘要：
Airborne Wind Energy is a lightweight technology that allows power extraction from the wind using airborne devices such as kites and gliders, where the airfoil orientation can be dynamically controlled in order to maximize performance. The dynamical complexity of turbulent aerodynamics makes this optimization problem unapproachable by conventional methods such as classical control theory, which rely on accurate and tractable analytical models of the dynamical system at hand. Here we propose to attack this problem through Reinforcement Learning, a technique that -- by repeated trial-and-error interactions with the environment -- learns to associate observations with profitable actions without requiring prior knowledge of the system. We show that in a simulated environment Reinforcement Learning finds an efficient way to control a kite so that it can tow a vehicle for long distances. The algorithm we use is based on a small set of intuitive observations and its physically transparent interpretation allows to describe the approximately optimal strategy as a simple list of manoeuvring instructions.
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