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 verification experiments are carried out in the static obstacle environment. The test results ...
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...
However, the authors noted that the success of the algorithm might not be guaranteed when increasing the size of the environment, suggesting the need for more complex algorithms such as a DQN. 4.3. Multi-agent Q-learning In a typical Q-learning system, each agent controls its own action ...
In order to address complex Three-dimensional (3D) obstacle environments, a novel aircraft trajectory planning method is proposed, which combines deep reinforcement learning with improved artificial potential fields. In this method, an improved artificial potential field (APF) algorithm is used as the ...
(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...
improved reward and punishment function is designed to improve the convergence speed of the algorithm and optimize the path so that the robot can plan a safer path while avoiding obstacles first. Compared with Deep Q Network(DQN) algorithm, the convergence speed after improvement is shortened by ...
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...
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL Wonjoon Goo and Scott Niekum. CoRL, 2022. S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning Samarth Sinha and Animesh Garg. CoRL, 2022. A Workflow for Offline Model-Free Robotic Reinforcement Learning...
The performance of the proposed approach improved resilience by 30%, compared with the strength-based pole replacement strategy by the U.S. National Electric Safety Code (NESC) strategy. Ji et al. [116] developed an energy management approach for microgrids through a DQN algorithm. The proposed...
Besides, D3QN significantly outperformed DQN in terms of the accumulated reward and learning speed. Tong et al. (2020) propose a deep Q-learning task scheduling (DQTS) that combines the advantages of the Q-learning algorithm and a deep neural network. This approach is aimed at solving the ...