By comparing the advantages of DQ, Double DQN, Dueling DQN and PER algorithm, IDQNPER algorithm is used to train the automatic path planning of intelligent driving vehicles. Finally, the simulation and verifica
An Improved Algorithm of Robot Path Planning in Complex Environment Based on Double DQN Deep Q Network (DQN) has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment. The reward function may be hard to model, and successful exp...
Online algorithm means the path planning takes place on-board, on the other hand, offline planning takes place off-board before the flight begins. The global goal of the flight may be to cover the whole area or to plan an optimal path between start and end positions. The remaining terms ...
Lv L, Zhang S, Ding D et al (2019) Path Planning via an Improved DQN-Based Learning Policy. IEEE Access 7:67319–67330. https://doi.org/10.1109/ACCESS.2019.2918703 Article MATH Google Scholar Sharma J, Andersen PA, Granmo OC et al (2021) Deep Q-Learning With Q-Matrix Transfer Lear...
(2015) reached human performance playing Atari games using a combination of Q-Learning and a neural network (the deep Q-Network algorithm, DQN). The Alpha-Go program (Silver et al., 2017), the world’s best Go player, is a combination of actor, critic and planning methods using NN to...
Random forest is a commonly-used machine learning algorithm, which combines the output of multiple decision trees to reach a single result. A decision tree in a forest cannot be pruned for sampling and therefore, prediction selection. Its ease of use and flexibility have fueled its adoption, as...
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image regist...
Finally, to validate the effectiveness of our proposed method, we use the DQfD algorithm and the DQN algorithm to perform PT path planning under scenarios containing elements of network defence deception (equipped with honeypots), respectively, and the performance of the algorithms is evaluated by ...
They took into account the possible USV's steering heading angles in the process of planning, turning the original two-dimensional (x, y) planning space into three-dimensional (x, y, θ). At the same time, they improved the A* algorithm by adding the radius of curvature into actual cost...
algorithm can reach the target point in a shorter path. To improve the ability to avoid dynamic obstacles,Liang et al. (2021)applied an improved A* algorithm with a minimum course alteration(MCA) algorithm to realize path planning. In this study, only important waypoints are retained to ...