Vehicle path planning method based on reinforcement learning

Abstract

The invention provides a vehicle path planning method based on reinforcement learning, which comprises the following steps: taking a state sequence of a client node as input information, sending the input information to a decision network, and selecting an action by the decision network according to an action value function and calculating and planning a vehicle traveling path. According to the method, the model is trained through the reinforcement learning algorithm based on historical distribution data, and therefore the purpose of dynamically planning the driving path under the condition that the road traffic condition and the number of distribution target nodes are changed is achieved. According to the method, complex and changeable road traffic conditions and delivery tasks with uncertain delivery target numbers in real life are considered, and the driving route is dynamically adjusted, so that the transportation efficiency is improved, and the cost is reduced.

Description

technical field [0001] The present invention relates to the field of intelligent transportation, in particular, to a vehicle path planning method based on reinforcement learning. Background technique [0002] Effective planning of vehicle routes is an important link in logistics management, public transportation and taxi passenger transportation, and operations in related fields, which helps to improve transportation efficiency and reduce costs. [0003] Oriol Vinyals et al. (Vinyals O, Fortunato M, Jaitly N. Pointer networks[C] Advances in Neural Information Processing Systems.2015:2692-2700.) proposed a simple but effective architecture called Pointer Net to learn combinatorial optimization problems , the model first uses machine learning methods to solve combinatorial optimization problems. Based on sequence-to-sequence and Neural Turing Machines, it uses a neural attention mechanism to solve the problem of variable-size output dictionaries, and uses supervised learning t...

AI Technical Summary

Problems solved by technology

[0007] According to the technical problem that the existing method proposed above does not match the actual situation that the traffic situation changes in each time period and the number of distribution nodes is uncertain, a vehicle path planning method based on reinforcement learning is provided.

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