anniversary
发表于 2025-3-26 22:48:24
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移动
发表于 2025-3-27 04:39:36
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不遵守
发表于 2025-3-27 06:28:21
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不怕任性
发表于 2025-3-27 11:14:22
Prediction of Robot Localization States Using Hidden Markov Models,ate the map of an abandoned coal mine. The prediction of the robot localization with precision still a big problem, for this reason, our study consists of a robot, which can move within an area of 9 squares. This robot is equipped with the sensing system, which detects obstacles in four directions:
令人发腻
发表于 2025-3-27 17:14:10
A New Approach for Multi-agent Reinforcement Learning,-error, the agent has to learn to maximize its total accumulated reward. Several algorithms and techniques were developed for a single agent reinforcement learning. Our purpose is to benefit from all done work in reinforcement learning of an agent and extend it to multi-agent system. we have propose
vanquish
发表于 2025-3-27 20:10:53
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脆弱带来
发表于 2025-3-27 23:56:08
2194-5357 eedings of the Artificial Intelligence & Industrial Applicat.This book gathers the refereed proceedings of the Artificial Intelligence and Industrial Applications (A2IA’2020), the first installment of an annual international conference organized by the ENSAM-Meknes at Moulay Ismail University, Moroc
北极人
发表于 2025-3-28 02:53:04
Criticism of the Twelve Masters,ing algorithms, as an alternative to classic methods. Indeed, the classical approaches are based on a pre-established policy of vehicle movement rules, while our method deduces the movement rules based on trial/error reinforcement learning approach, and effectively gives good results in relatively small moving areas and a limited number of agents.
Harridan
发表于 2025-3-28 08:49:55
On the Influence of Confucianism,s of a robot, which can move within an area of 9 squares. This robot is equipped with the sensing system, which detects obstacles in four directions: north, south, east and west. The sensors have an error rate of e = 25%. The objective of this work is the prediction of the robot localization using Hidden Markov Model.
很是迷惑
发表于 2025-3-28 12:52:45
Deep Learning Approach for Automated Guided Vehicle System,ing algorithms, as an alternative to classic methods. Indeed, the classical approaches are based on a pre-established policy of vehicle movement rules, while our method deduces the movement rules based on trial/error reinforcement learning approach, and effectively gives good results in relatively small moving areas and a limited number of agents.