KDD 2018滴滴派单算法论文。
We present a novel order dispatch algorithm in large-scale on-
demand ride-hailing platforms. While traditional order dispatch
approaches usually focus on immediate customer satisfaction, the
proposed algorithm is designed to provide a more efficient way to
optimize resource utilization and user experience in a global and
more farsighted view. In particular, we model order dispatch as a
large-scale sequential decision-making problem, where the decision
of assigning an order to a driver is determined by a centralized algo-
rithm in a coordinated way. The problem is solved in a learning and
planning manner: 1) based on historical data, we first summarize
demand and supply patterns into a spatiotemporal quantization,
each of which indicates the expected value of a driver being in a
particular state; 2) a planning step is conducted in real-time, where
each driver-order-pair is valued in consideration of both immedi-
ate rewards and future gains, and then dispatch is solved using
a combinatorial optimizing algorithm. Through extensive offline
experiments and online AB tests, the proposed approach delivers
remarkable improvement on the platform’s efficiency and has been
successfully deployed in the production system of Didi Chuxing.
2021-02-20 18:45:47
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关于强化学习基本概念的介绍，是一个很不错的英文书
2021-02-18 14:08:23
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自动驾驶车辆的本质是轮式移动机器人，是一个集模式识别、环境感知、规划决策和智能控制等功能于一体的综合系统。人工智能和机器学习领域的进步极大推动了自动驾驶技术的发展。当前主流的机器学习方法分为：监督学习、非监督学习和强化学习3种。强化学习方法更适用于复杂交通场景下自动驾驶系统决策和控制的智能处理，有利于提高自动驾驶的舒适性和安全性。
2021-02-18 14:08:23
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