Rapid path planning method based on variant dual DQNs (deep Q-networks) and mobile robot

Abstract

The invention discloses a rapid path planning method based on variant dual DQNs (deep Q-networks) and a mobile robot, wherein the mobile robot samples mini-batch conversion information from an experience replay storage; one of two dueling deep convolutional neural networks is selected as a first online network according to first preset rules, with the other serving as a first target network; predicted online operate value function Q(s, a; w) and greedy operation a' are acquired; maximum value of the predicted target operate value function is acquired; a loss function on current time step is calculated according to the maximum value of the predicted target operate value function and the predicted online operate value function; online weight parameter w is updated via an adaptive moment estimation method according to the loss function. The weight parameter updating mode based on dual Q learning and dueling DQN, and path planning is more effectively achieved for the mobile robot.

Description

technical field [0001] The present invention relates to the fields of machine learning and artificial intelligence, specifically, the present invention is a fast path planning method based on mutation-based double DQN. Background technique [0002] The path planning of the mobile robot means that the robot perceives the environment and autonomously plans a route to the target based on the information obtained by the sensor camera. In practice, people generally use methods based on traditional algorithms such as ant colony algorithm. However, with the continuous development of science and technology, the environment faced by mobile robots is becoming more and more complex and changeable. Traditional path planning methods can no longer meet the needs of mobile robots. need. [0003] In response to this situation, people proposed Deep Reinforcement Learning (DRL), which integrates deep learning and reinforcement learning, in which deep learning is mainly responsible for using ...

AI Technical Summary

Problems solved by technology

In practice, people generally use methods based on traditional algorithms such as ant colony algorithm. However, with the continuous development of science and technology, the environment faced by mobile robots is becoming more and more complex and changeable. Traditional path planning methods can no longer meet the needs of mobile robots. need

[0003] In response to this situation, people proposed Deep Reinforcement Learning (DRL), which integrates deep learning and reinforcement learning, in which deep learning is mainly responsible for using the perception function of the neural network to extract features from the input environment state. , to realize the fitting of the environment state to the state-action value function; while the reinforcement learning is responsible for completing the decision-making according to the output of the deep neural network and a certain exploration strategy, so as to realize the mapping from the state to the action, which can better meet the needs of mobile robots. Generally, path planning is based on the classic DQN algorithm in DRL. However, the DQN algorithm has the disadvantage of overestimating the action value.

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